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From our initial evaluation of O1 with the OlympicArena datasets to the construction of long thoughts, our work has involved numerous attempts, iterations, and in-depth analysis of O1\u2019s capabilities."}),"\n",(0,n.jsx)(t.p,{children:"Each question in this chapter highlights a critical aspect of O1\u2019s cognitive processes. We start by examining the structure of O1\u2019s thoughts and progress to more advanced concepts, such as the development of reward models, on-policy reasoning trees, and how these elements come together in constructing long thoughts. Our methodology, as depicted in our research timeline, emphasizes iterative evaluation and training strategies, combining both quantitative assessments and human feedback."}),"\n",(0,n.jsx)(t.p,{children:"This question-driven format not only narrates our technical journey but also reflects the broader \u201cjourney learning\u201d paradigm we follow, where the process of exploration is just as important as the final outcomes. 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A timeline tracks our progress chronologically, with research activities flowing vertically in the main diagram. Following the o1 release, we progressed from initial evaluation and knowledge acquisition to team assembly and analysis. Our exploration then focused on four long thought construction attempts: (1) Propose-Critique Loop, (2) Tree Search with LLM and Reward, (3) Multi-Agent Approach, and (4) Complete Human Thought Documentation. The second attempt, our core exploration, splits into three tracks: Process-Level Reward Model, Construction of Reasoning Tree, and Integrating Reasoning Tree into Long Thought. These converge in an iterative cycle of model improvement, including both quantitative and qualitative evaluation. The diagram's right side illustrates our training pipeline, featuring pre-training, iterative training, and optimization techniques. Solid black elements represent completed paths and milestones, while gray dashed elements indicate planned future explorations. 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Words per Line"})}),(0,s.jsx)(t.th,{children:(0,s.jsx)(t.strong,{children:"Keyword Count"})})]})}),(0,s.jsxs)(t.tbody,{children:[(0,s.jsxs)(t.tr,{children:[(0,s.jsx)(t.td,{children:"Cipher"}),(0,s.jsx)(t.td,{children:"8915"}),(0,s.jsx)(t.td,{children:"668"}),(0,s.jsx)(t.td,{children:"4.29"}),(0,s.jsx)(t.td,{children:"So: 31, First: 27, of: 27, Alternatively: 21, Second: 19, Third: 15, But: 15, Wait: 13, Alternatively perhaps: 13, Let: 12, and: 12, let: 11, first: 9, Now: 9, Think step: 8, step by: 8, by step: 8, So the: 8, the first: 8, Similarly: 6, as: 6, or: 5, we need to: 5, Option: 4, maybe: 4"})]}),(0,s.jsxs)(t.tr,{children:[(0,s.jsx)(t.td,{children:"Coding"}),(0,s.jsx)(t.td,{children:"3259"}),(0,s.jsx)(t.td,{children:"197"}),(0,s.jsx)(t.td,{children:"3.64"}),(0,s.jsx)(t.td,{children:"and: 8, as: 7, then: 4, For: 4, Now: 4, So: 3, Let: 3, Since: 3, We: 3, row=: 3, step by: 2, by step: 2, We need: 2, Let me: 2, step by step: 2, We need to: 2, Let me try: 2"})]}),(0,s.jsxs)(t.tr,{children:[(0,s.jsx)(t.td,{children:"Crossword"}),(0,s.jsx)(t.td,{children:"5311"}),(0,s.jsx)(t.td,{children:"396"}),(0,s.jsx)(t.td,{children:"5.75"}),(0,s.jsx)(t.td,{children:"Across: 37, So: 33, From: 31, and: 25, first: 19, Position: 19, we: 15, Now: 13, Possible: 13, as: 7, But: 7, Similarly: 7, third: 7, First: 6, So we: 6, Given: 5, Now let: 5, We: 4"})]}),(0,s.jsxs)(t.tr,{children:[(0,s.jsx)(t.td,{children:"English"}),(0,s.jsx)(t.td,{children:"757"}),(0,s.jsx)(t.td,{children:"49"}),(0,s.jsx)(t.td,{children:"9.88"}),(0,s.jsx)(t.td,{children:"the: 20, that: 15, to: 15, is: 13, because: 8, why: 7, Option because: 5, and: 4"})]}),(0,s.jsxs)(t.tr,{children:[(0,s.jsx)(t.td,{children:"Health Science"}),(0,s.jsx)(t.td,{children:"1010"}),(0,s.jsx)(t.td,{children:"86"}),(0,s.jsx)(t.td,{children:"6.14"}),(0,s.jsx)(t.td,{children:"and: 11, So: 5, Also: 3, But: 3, First: 2, Then: 2, So the: 2, but also: 2"})]}),(0,s.jsxs)(t.tr,{children:[(0,s.jsx)(t.td,{children:"Math"}),(0,s.jsx)(t.td,{children:"18751"}),(0,s.jsx)(t.td,{children:"521"}),(0,s.jsx)(t.td,{children:"9.49"}),(0,s.jsx)(t.td,{children:"Therefore: 48, But: 42, So: 38, Thus: 36, Similarly: 33, we: 26, and: 17, since: 16, for: 15, real: 15, Wait: 14, Let: 10, but: 9, Let me: 9, all: 8, k1: 8, Given: 8, Wait but: 8, Alternatively: 7, we can: 7, So the: 7, Then: 6, Given that: 6"})]}),(0,s.jsxs)(t.tr,{children:[(0,s.jsx)(t.td,{children:"Safety"}),(0,s.jsx)(t.td,{children:"510"}),(0,s.jsx)(t.td,{children:"41"}),(0,s.jsx)(t.td,{children:"8.27"}),(0,s.jsx)(t.td,{children:"So: 6, and: 65, But: 3, Also: 2, ChatGPT: 1, Write: 1, Explain: 1"})]}),(0,s.jsxs)(t.tr,{children:[(0,s.jsx)(t.td,{children:"Science"}),(0,s.jsx)(t.td,{children:"2411"}),(0,s.jsx)(t.td,{children:"91"}),(0,s.jsx)(t.td,{children:"7.62"}),(0,s.jsx)(t.td,{children:"and: 14, can: 6, compute: 6, But: 5, So: 4, Now: 3, Given: 2, but: 2, so: 2, Alternatively: 2"})]})]})]}),"\n",(0,s.jsx)(t.p,{children:(0,s.jsx)(t.em,{children:"Statistical summary of various examples from OpenAI O1\u2019s thought process across different domains. The table presents key metrics including the token count, the line count, the average number of words per line, and the frequency of the highest-occurring words or phrases derived using the n-gram algorithm. These keywords reflect the structure and style of the reasoning process, highlighting how the model introduces logical steps, alternatives, or corrections in different contexts."})}),"\n",(0,s.jsx)(t.p,{children:"Our analysis focuses on O1\u2019s reasoning examples provided by OpenAI, which includes eight instances of problem-solving steps for complex tasks. We categorized these examples by problem type and difficulty, observing that as problem difficulty increases, so does the length of the model\u2019s responses in both tokens and lines. This trend suggests that more challenging problems require more reasoning steps."}),"\n",(0,s.jsx)(t.p,{children:"Beyond just response length, we analyzed keyword frequencies. Words like \u201cconsider,\u201d \u201cif,\u201d and \u201cpossible\u201d frequently appear in more complex problems, indicating the model is exploring multiple solution paths. Keywords such as \u201cwait\u201d and \u201calternatively\u201d signal the model\u2019s ability to reflect and self-correct, suggesting a deeper, non-linear reasoning process."}),"\n",(0,s.jsx)(t.p,{children:"Additionally, we manually reviewed O1\u2019s approach to solving mathematical problems, revealing a methodical thought process. The model uses iterative problem-solving techniques, breaking down complex equations, and frequently reassessing its steps for consistency. It also tests multiple hypotheses and concludes by verifying its final solution, ensuring accuracy and reliability in its reasoning."})]})}function c(e={}){const{wrapper:t}={...(0,i.R)(),...e.components};return t?(0,s.jsx)(t,{...e,children:(0,s.jsx)(h,{...e})}):h(e)}},7239:(e,t,n)=>{n.d(t,{A:()=>s});const s=n.p+"assets/images/o1_example-ca3ae43f9e0d50f126f867c088dfdab3.png"},8453:(e,t,n)=>{n.d(t,{R:()=>o,x:()=>l});var s=n(6540);const i={},r=s.createContext(i);function o(e){const t=s.useContext(r);return s.useMemo((function(){return"function"==typeof e?e(t):{...t,...e}}),[t,e])}function l(e){let t;return t=e.disableParentContext?"function"==typeof e.components?e.components(i):e.components||i:o(e.components),s.createElement(r.Provider,{value:t},e.children)}}}]); \ No newline at end of file diff --git a/assets/js/bf2bed40.d6e62461.js b/assets/js/bf2bed40.d6e62461.js new file mode 100644 index 0000000..3e3dd86 --- /dev/null +++ b/assets/js/bf2bed40.d6e62461.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkwalnut_plan=self.webpackChunkwalnut_plan||[]).push([[8316],{7940:(n,e,t)=>{t.r(e),t.d(e,{assets:()=>l,contentTitle:()=>s,default:()=>d,frontMatter:()=>i,metadata:()=>r,toc:()=>c});var o=t(4848),a=t(8453);const i={sidebar_position:1},s="What would be an effective annotation strategy for human-ai collaboration?",r={id:"projects/o1_replicate/exploration_journey/human_annotation",title:"What would be an effective annotation strategy for human-ai collaboration?",description:"We have developed a human-AI pipeline that generates high-quality, long-form reasoning data based on the MATH dataset, following our \u201cjourney learning\u201d paradigm. 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256&t.flags?(t.flags&=-257,zl(e,t,l,r=cl(Error(a(422))))):null!==t.memoizedState?(t.child=e.child,t.flags|=128,null):(i=r.fallback,o=t.mode,r=Iu({mode:"visible",children:r.children},o,0,null),(i=Du(i,o,l,null)).flags|=2,r.return=t,i.return=t,r.sibling=i,t.child=r,1&t.mode&&ka(t,e.child,null,l),t.child.memoizedState=Il(l),t.memoizedState=Dl,i);if(!(1&t.mode))return zl(e,t,l,null);if("$!"===o.data){if(r=o.nextSibling&&o.nextSibling.dataset)var s=r.dgst;return r=s,zl(e,t,l,r=cl(i=Error(a(419)),r,void 0))}if(s=!!(l&e.childLanes),vl||s){if(null!==(r=Ts)){switch(l&-l){case 4:o=2;break;case 16:o=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:o=32;break;case 536870912:o=268435456;break;default:o=0}0!==(o=o&(r.suspendedLanes|l)?0:o)&&o!==i.retryLane&&(i.retryLane=o,Da(e,o),nu(r,e,o,-1))}return 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t=e.languages.javascript,n=/\{(?:[^{}]|\{(?:[^{}]|\{[^{}]*\})*\})+\}/.source,r="(@(?:arg|argument|param|property)\\s+(?:"+n+"\\s+)?)";e.languages.jsdoc=e.languages.extend("javadoclike",{parameter:{pattern:RegExp(r+/(?:(?!\s)[$\w\xA0-\uFFFF.])+(?=\s|$)/.source),lookbehind:!0,inside:{punctuation:/\./}}}),e.languages.insertBefore("jsdoc","keyword",{"optional-parameter":{pattern:RegExp(r+/\[(?:(?!\s)[$\w\xA0-\uFFFF.])+(?:=[^[\]]+)?\](?=\s|$)/.source),lookbehind:!0,inside:{parameter:{pattern:/(^\[)[$\w\xA0-\uFFFF\.]+/,lookbehind:!0,inside:{punctuation:/\./}},code:{pattern:/(=)[\s\S]*(?=\]$)/,lookbehind:!0,inside:t,alias:"language-javascript"},punctuation:/[=[\]]/}},"class-name":[{pattern:RegExp(/(@(?:augments|class|extends|interface|memberof!?|template|this|typedef)\s+(?:<TYPE>\s+)?)[A-Z]\w*(?:\.[A-Z]\w*)*/.source.replace(/<TYPE>/g,(function(){return 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n=["function","function-variable","method","method-variable","property-access"],r=0;r<n.length;r++){var o=n[r],a=e.languages.javascript[o];o=(a="RegExp"===e.util.type(a)?e.languages.javascript[o]={pattern:a}:a).inside||{};(a.inside=o)["maybe-class-name"]=/^[A-Z][\s\S]*/}}(C),function(e){var t=e.util.clone(e.languages.javascript),n=/(?:\s|\/\/.*(?!.)|\/\*(?:[^*]|\*(?!\/))\*\/)/.source,r=/(?:\{(?:\{(?:\{[^{}]*\}|[^{}])*\}|[^{}])*\})/.source,o=/(?:\{<S>*\.{3}(?:[^{}]|<BRACES>)*\})/.source;function a(e,t){return e=e.replace(/<S>/g,(function(){return n})).replace(/<BRACES>/g,(function(){return r})).replace(/<SPREAD>/g,(function(){return o})),RegExp(e,t)}function i(t){for(var n=[],r=0;r<t.length;r++){var o=t[r],a=!1;"string"!=typeof 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e.languages.kotlin.string,e.languages.insertBefore("kotlin","keyword",{annotation:{pattern:/\B@(?:\w+:)?(?:[A-Z]\w*|\[[^\]]+\])/,alias:"builtin"}}),e.languages.insertBefore("kotlin","function",{label:{pattern:/\b\w+@|@\w+\b/,alias:"symbol"}}),e.languages.kt=e.languages.kotlin,e.languages.kts=e.languages.kotlin}(C),C.languages.c=C.languages.extend("clike",{comment:{pattern:/\/\/(?:[^\r\n\\]|\\(?:\r\n?|\n|(?![\r\n])))*|\/\*[\s\S]*?(?:\*\/|$)/,greedy:!0},string:{pattern:/"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},"class-name":{pattern:/(\b(?:enum|struct)\s+(?:__attribute__\s*\(\([\s\S]*?\)\)\s*)?)\w+|\b[a-z]\w*_t\b/,lookbehind:!0},keyword:/\b(?:_Alignas|_Alignof|_Atomic|_Bool|_Complex|_Generic|_Imaginary|_Noreturn|_Static_assert|_Thread_local|__attribute__|asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|inline|int|long|register|return|short|signed|sizeof|static|struct|switch|typedef|typeof|union|unsigned|void|volatile|while)\b/,function:/\b[a-z_]\w*(?=\s*\()/i,number:/(?:\b0x(?:[\da-f]+(?:\.[\da-f]*)?|\.[\da-f]+)(?:p[+-]?\d+)?|(?:\b\d+(?:\.\d*)?|\B\.\d+)(?:e[+-]?\d+)?)[ful]{0,4}/i,operator:/>>=?|<<=?|->|([-+&|:])\1|[?:~]|[-+*/%&|^!=<>]=?/}),C.languages.insertBefore("c","string",{char:{pattern:/'(?:\\(?:\r\n|[\s\S])|[^'\\\r\n]){0,32}'/,greedy:!0}}),C.languages.insertBefore("c","string",{macro:{pattern:/(^[\t 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C.languages.c.boolean,C.languages.objectivec=C.languages.extend("c",{string:{pattern:/@?"(?:\\(?:\r\n|[\s\S])|[^"\\\r\n])*"/,greedy:!0},keyword:/\b(?:asm|auto|break|case|char|const|continue|default|do|double|else|enum|extern|float|for|goto|if|in|inline|int|long|register|return|self|short|signed|sizeof|static|struct|super|switch|typedef|typeof|union|unsigned|void|volatile|while)\b|(?:@interface|@end|@implementation|@protocol|@class|@public|@protected|@private|@property|@try|@catch|@finally|@throw|@synthesize|@dynamic|@selector)\b/,operator:/-[->]?|\+\+?|!=?|<<?=?|>>?=?|==?|&&?|\|\|?|[~^%?*\/@]/}),delete C.languages.objectivec["class-name"],C.languages.objc=C.languages.objectivec,C.languages.reason=C.languages.extend("clike",{string:{pattern:/"(?:\\(?:\r\n|[\s\S])|[^\\\r\n"])*"/,greedy:!0},"class-name":/\b[A-Z]\w*/,keyword:/\b(?:and|as|assert|begin|class|constraint|do|done|downto|else|end|exception|external|for|fun|function|functor|if|in|include|inherit|initializer|lazy|let|method|module|mutable|new|nonrec|object|of|open|or|private|rec|sig|struct|switch|then|to|try|type|val|virtual|when|while|with)\b/,operator:/\.{3}|:[:=]|\|>|->|=(?:==?|>)?|<=?|>=?|[|^?'#!~`]|[+\-*\/]\.?|\b(?:asr|land|lor|lsl|lsr|lxor|mod)\b/}),C.languages.insertBefore("reason","class-name",{char:{pattern:/'(?:\\x[\da-f]{2}|\\o[0-3][0-7][0-7]|\\\d{3}|\\.|[^'\\\r\n])'/,greedy:!0},constructor:/\b[A-Z]\w*\b(?!\s*\.)/,label:{pattern:/\b[a-z]\w*(?=::)/,alias:"symbol"}}),delete C.languages.reason.function,function(e){for(var t=/\/\*(?:[^*/]|\*(?!\/)|\/(?!\*)|<self>)*\*\//.source,n=0;n<2;n++)t=t.replace(/<self>/g,(function(){return t}));t=t.replace(/<self>/g,(function(){return/[^\s\S]/.source})),e.languages.rust={comment:[{pattern:RegExp(/(^|[^\\])/.source+t),lookbehind:!0,greedy:!0},{pattern:/(^|[^\\:])\/\/.*/,lookbehind:!0,greedy:!0}],string:{pattern:/b?"(?:\\[\s\S]|[^\\"])*"|b?r(#*)"(?:[^"]|"(?!\1))*"\1/,greedy:!0},char:{pattern:/b?'(?:\\(?:x[0-7][\da-fA-F]|u\{(?:[\da-fA-F]_*){1,6}\}|.)|[^\\\r\n\t'])'/,greedy:!0},attribute:{pattern:/#!?\[(?:[^\[\]"]|"(?:\\[\s\S]|[^\\"])*")*\]/,greedy:!0,alias:"attr-name",inside:{string:null}},"closure-params":{pattern:/([=(,:]\s*|\bmove\s*)\|[^|]*\||\|[^|]*\|(?=\s*(?:\{|->))/,lookbehind:!0,greedy:!0,inside:{"closure-punctuation":{pattern:/^\||\|$/,alias:"punctuation"},rest:null}},"lifetime-annotation":{pattern:/'\w+/,alias:"symbol"},"fragment-specifier":{pattern:/(\$\w+:)[a-z]+/,lookbehind:!0,alias:"punctuation"},variable:/\$\w+/,"function-definition":{pattern:/(\bfn\s+)\w+/,lookbehind:!0,alias:"function"},"type-definition":{pattern:/(\b(?:enum|struct|trait|type|union)\s+)\w+/,lookbehind:!0,alias:"class-name"},"module-declaration":[{pattern:/(\b(?:crate|mod)\s+)[a-z][a-z_\d]*/,lookbehind:!0,alias:"namespace"},{pattern:/(\b(?:crate|self|super)\s*)::\s*[a-z][a-z_\d]*\b(?:\s*::(?:\s*[a-z][a-z_\d]*\s*::)*)?/,lookbehind:!0,alias:"namespace",inside:{punctuation:/::/}}],keyword:[/\b(?:Self|abstract|as|async|await|become|box|break|const|continue|crate|do|dyn|else|enum|extern|final|fn|for|if|impl|in|let|loop|macro|match|mod|move|mut|override|priv|pub|ref|return|self|static|struct|super|trait|try|type|typeof|union|unsafe|unsized|use|virtual|where|while|yield)\b/,/\b(?:bool|char|f(?:32|64)|[ui](?:8|16|32|64|128|size)|str)\b/],function:/\b[a-z_]\w*(?=\s*(?:::\s*<|\())/,macro:{pattern:/\b\w+!/,alias:"property"},constant:/\b[A-Z_][A-Z_\d]+\b/,"class-name":/\b[A-Z]\w*\b/,namespace:{pattern:/(?:\b[a-z][a-z_\d]*\s*::\s*)*\b[a-z][a-z_\d]*\s*::(?!\s*<)/,inside:{punctuation:/::/}},number:/\b(?:0x[\dA-Fa-f](?:_?[\dA-Fa-f])*|0o[0-7](?:_?[0-7])*|0b[01](?:_?[01])*|(?:(?:\d(?:_?\d)*)?\.)?\d(?:_?\d)*(?:[Ee][+-]?\d+)?)(?:_?(?:f32|f64|[iu](?:8|16|32|64|size)?))?\b/,boolean:/\b(?:false|true)\b/,punctuation:/->|\.\.=|\.{1,3}|::|[{}[\];(),:]/,operator:/[-+*\/%!^]=?|=[=>]?|&[&=]?|\|[|=]?|<<?=?|>>?=?|[@?]/},e.languages.rust["closure-params"].inside.rest=e.languages.rust,e.languages.rust.attribute.inside.string=e.languages.rust.string}(C),C.languages.go=C.languages.extend("clike",{string:{pattern:/(^|[^\\])"(?:\\.|[^"\\\r\n])*"|`[^`]*`/,lookbehind:!0,greedy:!0},keyword:/\b(?:break|case|chan|const|continue|default|defer|else|fallthrough|for|func|go(?:to)?|if|import|interface|map|package|range|return|select|struct|switch|type|var)\b/,boolean:/\b(?:_|false|iota|nil|true)\b/,number:[/\b0(?:b[01_]+|o[0-7_]+)i?\b/i,/\b0x(?:[a-f\d_]+(?:\.[a-f\d_]*)?|\.[a-f\d_]+)(?:p[+-]?\d+(?:_\d+)*)?i?(?!\w)/i,/(?:\b\d[\d_]*(?:\.[\d_]*)?|\B\.\d[\d_]*)(?:e[+-]?[\d_]+)?i?(?!\w)/i],operator:/[*\/%^!=]=?|\+[=+]?|-[=-]?|\|[=|]?|&(?:=|&|\^=?)?|>(?:>=?|=)?|<(?:<=?|=|-)?|:=|\.\.\./,builtin:/\b(?:append|bool|byte|cap|close|complex|complex(?:64|128)|copy|delete|error|float(?:32|64)|u?int(?:8|16|32|64)?|imag|len|make|new|panic|print(?:ln)?|real|recover|rune|string|uintptr)\b/}),C.languages.insertBefore("go","string",{char:{pattern:/'(?:\\.|[^'\\\r\n]){0,10}'/,greedy:!0}}),delete 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b/blog/archive/index.html @@ -0,0 +1,14 @@ +<!doctype html> +<html lang="en" dir="ltr" class="plugin-blog plugin-id-default" data-has-hydrated="false"> +<head> +<meta charset="UTF-8"> +<meta name="generator" content="Docusaurus v3.5.2"> +<title data-rh="true">Archive | Walnut Plan + + + + + +
Skip to main content
+ + \ No newline at end of file diff --git a/blog/atom.css b/blog/atom.css new file mode 100644 index 0000000..d2fc20b --- /dev/null +++ b/blog/atom.css @@ -0,0 +1,75 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +main { + flex: 1 0 auto; + width: 100%; + margin: 2rem auto; + max-width: 800px; + /* stylelint-disable-next-line font-family-name-quotes */ + font-family: system-ui, -apple-system, Segoe UI, Roboto, Ubuntu, Cantarell; +} + +.info { + display: block; + margin: 2rem 0; + padding: 1.6rem 2.4rem; + border: 1px solid dodgerblue; + border-left-width: 0.5rem; + border-radius: 0.4rem; + background-color: #edf5ff; +} + +a { + color: #005aff; + text-decoration: none; +} + +h1 { + text-wrap: balance; + font-size: 3.4rem; + font-weight: 800; + margin-bottom: 2rem; + display: flex; + align-items: center; +} + +h1 .rss-icon { + height: 3.2rem; + width: 3.2rem; + margin-right: 1rem; +} + +h2 { + font-size: 2.2rem; + font-weight: 700; + margin-bottom: 0.2rem; +} + +h3 { + font-size: 1.8rem; + font-weight: 700; + margin-bottom: 0.1rem; +} + +.blog-description { + font-size: 1.4rem; + margin-bottom: 0.6rem; +} + +.blog-post-date { + font-size: 1rem; + line-height: 1.4rem; + font-style: italic; + color: #797b7e; +} + +.blog-post-description { + font-size: 1rem; + line-height: 1.4rem; + color: #434349; +} diff --git a/blog/atom.xml b/blog/atom.xml new file mode 100644 index 0000000..5f5c0ed --- /dev/null +++ b/blog/atom.xml @@ -0,0 +1,109 @@ + + + https://huangzhen02.github.io/walnut-plan/blog + Walnut Plan Blog + 2021-08-26T00:00:00.000Z + https://github.com/jpmonette/feed + + Walnut Plan Blog + https://huangzhen02.github.io/walnut-plan/img/favicon.ico + + <![CDATA[Welcome]]> + https://huangzhen02.github.io/walnut-plan/blog/welcome + + 2021-08-26T00:00:00.000Z + + Docusaurus blogging features are powered by the blog plugin.

+

Here are a few tips you might find useful.

+

Simply add Markdown files (or folders) to the blog directory.

+

Regular blog authors can be added to authors.yml.

+

The blog post date can be extracted from filenames, such as:

+
    +
  • 2019-05-30-welcome.md
  • +
  • 2019-05-30-welcome/index.md
  • +
+

A blog post folder can be convenient to co-locate blog post images:

+

Docusaurus Plushie

+

The blog supports tags as well!

+

And if you don't want a blog: just delete this directory, and use blog: false in your Docusaurus config.

]]>
+ + Sébastien Lorber + https://sebastienlorber.com + + + Yangshun Tay + https://github.com/yangshun + + + + +
+ + <![CDATA[MDX Blog Post]]> + https://huangzhen02.github.io/walnut-plan/blog/mdx-blog-post + + 2021-08-01T00:00:00.000Z + + Blog posts support Docusaurus Markdown features, such as MDX.

+
tip

Use the power of React to create interactive blog posts.

+ +

For example, use JSX to create an interactive button:

+
<button onClick={() => alert('button clicked!')}>Click me!</button>
+]]>
+ + Sébastien Lorber + https://sebastienlorber.com + + +
+ + <![CDATA[Long Blog Post]]> + https://huangzhen02.github.io/walnut-plan/blog/long-blog-post + + 2019-05-29T00:00:00.000Z + + This is the summary of a very long blog post,

+

Use a <!-- truncate --> comment to limit blog post size in the list view.

+

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+

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+

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+

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+

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+

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+

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+

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+

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+

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+

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+

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+

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet

+

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+

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+

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]]>
+ + Yangshun Tay + https://github.com/yangshun + + + +
+ + <![CDATA[First Blog Post]]> + https://huangzhen02.github.io/walnut-plan/blog/first-blog-post + + 2019-05-28T00:00:00.000Z + + Lorem ipsum dolor sit amet...

+

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]]>
+ + Sébastien Lorber + https://sebastienlorber.com + + + Yangshun Tay + https://github.com/yangshun + + + +
+
\ No newline at end of file diff --git a/blog/atom.xsl b/blog/atom.xsl new file mode 100644 index 0000000..271895c --- /dev/null +++ b/blog/atom.xsl @@ -0,0 +1,92 @@ + + + + + + + + Atom Feed | <xsl:value-of + select="atom:feed/atom:title" + /> + + + +
+
+
+ This is an Atom feed. Subscribe by copying the URL + from the address bar into your newsreader. Visit + About Feeds to learn more + and get started. It’s free. +
+

+
+ + + + + + + + + + +
+ +

+

+ +

+
+

Recent Posts

+
+ +
+

+ +
+ +
+
+
+
+
+ + +
+
diff --git a/blog/authors/all-sebastien-lorber-articles/index.html b/blog/authors/all-sebastien-lorber-articles/index.html new file mode 100644 index 0000000..3ab6b45 --- /dev/null +++ b/blog/authors/all-sebastien-lorber-articles/index.html @@ -0,0 +1,16 @@ + + + + + +Sébastien Lorber - 3 posts | Walnut Plan + + + + + +
Skip to main content
Sébastien Lorber
Docusaurus maintainer
View all authors

+ + \ No newline at end of file diff --git a/blog/authors/index.html b/blog/authors/index.html new file mode 100644 index 0000000..e33de5b --- /dev/null +++ b/blog/authors/index.html @@ -0,0 +1,14 @@ + + + + + +Authors | Walnut Plan + + + + + +
Skip to main content

Authors

+ + \ No newline at end of file diff --git a/blog/authors/yangshun/index.html b/blog/authors/yangshun/index.html new file mode 100644 index 0000000..ac190d8 --- /dev/null +++ b/blog/authors/yangshun/index.html @@ -0,0 +1,16 @@ + + + + + +Yangshun Tay - 3 posts | Walnut Plan + + + + + +
Skip to main content
Yangshun Tay
Front End Engineer @ Facebook
View all authors

+ + \ No newline at end of file diff --git a/blog/first-blog-post/index.html b/blog/first-blog-post/index.html new file mode 100644 index 0000000..ea2d7e2 --- /dev/null +++ b/blog/first-blog-post/index.html @@ -0,0 +1,15 @@ + + + + + +First Blog Post | Walnut Plan + + + + + +
Skip to main content

First Blog Post

· One min read
Sébastien Lorber
Docusaurus maintainer
Yangshun Tay
Front End Engineer @ Facebook

Lorem ipsum dolor sit amet...

+

...consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet

+ + \ No newline at end of file diff --git a/blog/index.html b/blog/index.html new file mode 100644 index 0000000..1b1b36f --- /dev/null +++ b/blog/index.html @@ -0,0 +1,17 @@ + + + + + +Blog | Walnut Plan + + + + + +
Skip to main content
+ + \ No newline at end of file diff --git a/blog/long-blog-post/index.html b/blog/long-blog-post/index.html new file mode 100644 index 0000000..7caeda0 --- /dev/null +++ b/blog/long-blog-post/index.html @@ -0,0 +1,31 @@ + + + + + +Long Blog Post | Walnut Plan + + + + + +
Skip to main content

Long Blog Post

· 3 min read
Yangshun Tay
Front End Engineer @ Facebook

This is the summary of a very long blog post,

+

Use a <!-- truncate --> comment to limit blog post size in the list view.

+

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet

+

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet

+

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet

+

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet

+

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+

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+

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+

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+

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+

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+

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+

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+

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+

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+

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+

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+ + \ No newline at end of file diff --git a/blog/mdx-blog-post/index.html b/blog/mdx-blog-post/index.html new file mode 100644 index 0000000..54f69dd --- /dev/null +++ b/blog/mdx-blog-post/index.html @@ -0,0 +1,19 @@ + + + + + +MDX Blog Post | Walnut Plan + + + + + +
Skip to main content

MDX Blog Post

· One min read
Sébastien Lorber
Docusaurus maintainer

Blog posts support Docusaurus Markdown features, such as MDX.

+
tip

Use the power of React to create interactive blog posts.

+ +

For example, use JSX to create an interactive button:

+
<button onClick={() => alert('button clicked!')}>Click me!</button>
+
+ + \ No newline at end of file diff --git a/blog/rss.css b/blog/rss.css new file mode 100644 index 0000000..d2fc20b --- /dev/null +++ b/blog/rss.css @@ -0,0 +1,75 @@ +/** + * Copyright (c) Facebook, Inc. and its affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +main { + flex: 1 0 auto; + width: 100%; + margin: 2rem auto; + max-width: 800px; + /* stylelint-disable-next-line font-family-name-quotes */ + font-family: system-ui, -apple-system, Segoe UI, Roboto, Ubuntu, Cantarell; +} + +.info { + display: block; + margin: 2rem 0; + padding: 1.6rem 2.4rem; + border: 1px solid dodgerblue; + border-left-width: 0.5rem; + border-radius: 0.4rem; + background-color: #edf5ff; +} + +a { + color: #005aff; + text-decoration: none; +} + +h1 { + text-wrap: balance; + font-size: 3.4rem; + font-weight: 800; + margin-bottom: 2rem; + display: flex; + align-items: center; +} + +h1 .rss-icon { + height: 3.2rem; + width: 3.2rem; + margin-right: 1rem; +} + +h2 { + font-size: 2.2rem; + font-weight: 700; + margin-bottom: 0.2rem; +} + +h3 { + font-size: 1.8rem; + font-weight: 700; + margin-bottom: 0.1rem; +} + +.blog-description { + font-size: 1.4rem; + margin-bottom: 0.6rem; +} + +.blog-post-date { + font-size: 1rem; + line-height: 1.4rem; + font-style: italic; + color: #797b7e; +} + +.blog-post-description { + font-size: 1rem; + line-height: 1.4rem; + color: #434349; +} diff --git a/blog/rss.xml b/blog/rss.xml new file mode 100644 index 0000000..88554a8 --- /dev/null +++ b/blog/rss.xml @@ -0,0 +1,87 @@ + + + + Walnut Plan Blog + https://huangzhen02.github.io/walnut-plan/blog + Walnut Plan Blog + Thu, 26 Aug 2021 00:00:00 GMT + https://validator.w3.org/feed/docs/rss2.html + https://github.com/jpmonette/feed + en + + <![CDATA[Welcome]]> + https://huangzhen02.github.io/walnut-plan/blog/welcome + https://huangzhen02.github.io/walnut-plan/blog/welcome + Thu, 26 Aug 2021 00:00:00 GMT + + Docusaurus blogging features are powered by the blog plugin.

+

Here are a few tips you might find useful.

+

Simply add Markdown files (or folders) to the blog directory.

+

Regular blog authors can be added to authors.yml.

+

The blog post date can be extracted from filenames, such as:

+
    +
  • 2019-05-30-welcome.md
  • +
  • 2019-05-30-welcome/index.md
  • +
+

A blog post folder can be convenient to co-locate blog post images:

+

Docusaurus Plushie

+

The blog supports tags as well!

+

And if you don't want a blog: just delete this directory, and use blog: false in your Docusaurus config.

]]>
+ Facebook + Hello + Docusaurus +
+ + <![CDATA[MDX Blog Post]]> + https://huangzhen02.github.io/walnut-plan/blog/mdx-blog-post + https://huangzhen02.github.io/walnut-plan/blog/mdx-blog-post + Sun, 01 Aug 2021 00:00:00 GMT + + Blog posts support Docusaurus Markdown features, such as MDX.

+
tip

Use the power of React to create interactive blog posts.

+ +

For example, use JSX to create an interactive button:

+
<button onClick={() => alert('button clicked!')}>Click me!</button>
+]]>
+ Docusaurus +
+ + <![CDATA[Long Blog Post]]> + https://huangzhen02.github.io/walnut-plan/blog/long-blog-post + https://huangzhen02.github.io/walnut-plan/blog/long-blog-post + Wed, 29 May 2019 00:00:00 GMT + + This is the summary of a very long blog post,

+

Use a <!-- truncate --> comment to limit blog post size in the list view.

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+

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+

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+

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+

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+

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+

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]]>
+ Hello + Docusaurus +
+ + <![CDATA[First Blog Post]]> + https://huangzhen02.github.io/walnut-plan/blog/first-blog-post + https://huangzhen02.github.io/walnut-plan/blog/first-blog-post + Tue, 28 May 2019 00:00:00 GMT + + Lorem ipsum dolor sit amet...

+

...consectetur adipiscing elit. Pellentesque elementum dignissim ultricies. Fusce rhoncus ipsum tempor eros aliquam consequat. Lorem ipsum dolor sit amet

]]>
+ Hola + Docusaurus +
+
+
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diff --git a/blog/tags/docusaurus/index.html b/blog/tags/docusaurus/index.html new file mode 100644 index 0000000..38946e7 --- /dev/null +++ b/blog/tags/docusaurus/index.html @@ -0,0 +1,17 @@ + + + + + +4 posts tagged with "Docusaurus" | Walnut Plan + + + + + +
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4 posts tagged with "Docusaurus"

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+ + \ No newline at end of file diff --git a/blog/welcome/index.html b/blog/welcome/index.html new file mode 100644 index 0000000..148b613 --- /dev/null +++ b/blog/welcome/index.html @@ -0,0 +1,26 @@ + + + + + +Welcome | Walnut Plan + + + + + +
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Welcome

· One min read
Sébastien Lorber
Docusaurus maintainer
Yangshun Tay
Front End Engineer @ Facebook

Docusaurus blogging features are powered by the blog plugin.

+

Here are a few tips you might find useful.

+

Simply add Markdown files (or folders) to the blog directory.

+

Regular blog authors can be added to authors.yml.

+

The blog post date can be extracted from filenames, such as:

+
    +
  • 2019-05-30-welcome.md
  • +
  • 2019-05-30-welcome/index.md
  • +
+

A blog post folder can be convenient to co-locate blog post images:

+

Docusaurus Plushie

+

The blog supports tags as well!

+

And if you don't want a blog: just delete this directory, and use blog: false in your Docusaurus config.

+ + \ No newline at end of file diff --git a/docs/category/exploration-journey/index.html b/docs/category/exploration-journey/index.html new file mode 100644 index 0000000..50e24c5 --- /dev/null +++ b/docs/category/exploration-journey/index.html @@ -0,0 +1,14 @@ + + + + + +Exploration Journey | Walnut Plan + + + + + +
Skip to main content

Exploration Journey

📄️ How does Long Thought Work?

While we are still in the hypothesis stage without sufficient empirical evidence, we believe the success of O1’s long-thought approach is due to journey learning, as discussed earlier. Unlike shortcut learning, journey learning allows the model to explore the entire decision-making process, much like human problem-solving. O1 can consider multiple solution paths, learn from mistakes, and develop a deeper understanding of the problem—not just finding the correct answer but understanding why and how to reach it.

📄️ How to Evaluate our Trials?

In addition to testing accuracy scores using specific evaluation metrics on benchmarks, manually reviewing actual cases is a crucial step in evaluating data and models. Therefore, to provide a more intuitive way to evaluate the model’s performance on specific problems, we build a visual data analysis platform using Streamlit. Specifically, our visualization platform includes the visualization of synthetic trees and their corresponding long thoughts as well as the output of the trained model. Furthermore, when visualizing results, we support detailed conditional filtering, such as filtering for correctly or incorrectly answered questions, or whether the output contains keywords indicating reflection or hesitation (e.g., “wait”). Additionally, we support comparison between different iterations of synthetic data and model outputs, which makes it highly intuitive and helps us easily validate whether the new round of data or models is effective.

+ + \ No newline at end of file diff --git a/docs/category/o1-replication-journey/index.html b/docs/category/o1-replication-journey/index.html new file mode 100644 index 0000000..3198e08 --- /dev/null +++ b/docs/category/o1-replication-journey/index.html @@ -0,0 +1,14 @@ + + + + + +O1 Replication Journey | Walnut Plan + + + + + +
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+ + \ No newline at end of file diff --git a/docs/category/projects/index.html b/docs/category/projects/index.html new file mode 100644 index 0000000..0713fa9 --- /dev/null +++ b/docs/category/projects/index.html @@ -0,0 +1,14 @@ + + + + + +Projects | Walnut Plan + + + + + +
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+ + \ No newline at end of file diff --git a/docs/intro/index.html b/docs/intro/index.html new file mode 100644 index 0000000..4db1a76 --- /dev/null +++ b/docs/intro/index.html @@ -0,0 +1,41 @@ + + + + + +Introduction | Walnut Plan + + + + + +
Skip to main content

Introduction

+

Background

+

+

The Walnut Plan aspires to be an open pioneer in the research of complex reasoning and deep thinking capabilities in Artificial Intelligence. Our mission is to explore, develop, and share innovative technologies that significantly enhance the complex reasoning abilities of large language models, while advocating for the principles of open science and knowledge sharing. Through relentless efforts, we aim to drive the evolution of AI from simple information processing tools to intelligent systems capable of deep thinking at the level of "Newton" and "Einstein".

+

Mission

+

Guided by this grand vision, the Walnut Plan is committed to reshaping the paradigm of AI research. We believe that only through open, transparent, and collaborative approaches can we truly promote the democratization of AI technology for the benefit of all humanity. Our goals extend beyond technological breakthroughs to fostering a more open and responsible AI research ecosystem.

+

In the long term, the Walnut Plan will dedicate itself to developing a new generation of AI systems capable of assisting and even independently conducting scientific discoveries. We expect to accelerate the accumulation of human knowledge and innovation processes through the popularization of AI capabilities. At the same time, we are deeply aware of our significant responsibility and will always prioritize ensuring the safety and ethical development of AI, exploring new ways for AI and human intelligence to develop in synergy.

+

By establishing an interdisciplinary and inter-organizational research network, we aim to bring together top global talent to collectively tackle the frontier challenges in the AI field. We believe that only through collective wisdom and effort can we truly achieve leap-forward development in AI technology and make substantial contributions to human technological progress and social development.

+

The Walnut Plan is not just a research initiative, but a movement to drive the transformation of AI technology and scientific research culture. We invite all like-minded researchers, developers, and thinkers to join our ranks, to collectively write a new chapter in artificial intelligence, and to contribute to creating a more intelligent, open, and better future.

+

Modern AI Research

+

+

Modern AI research often involves prolonged, collaborative efforts that extend far beyond the traditional scope of individual, short-term projects. These endeavors frequently require large teams, extensive computational resources, and iterative experimentation over the course of months or even years. As a result, the traditional approach of publishing findings only at the end of the research cycle is no longer sufficient to meet the demands of the field.

+

Moreover, the fast pace of AI advancements and the need for reproducibility call for more open and continuous communication of research progress. Researchers are increasingly recognizing the value of sharing not just the final results but also the tools, datasets, methodologies, and even setbacks encountered along the way. This approach not only promotes transparency but also helps the broader research community to learn from challenges, adapt methodologies, and contribute to a collective knowledge base that accelerates progress across the field.

+

We represents a pioneering effort to reimagine the process of conducting and communicating AI research. By providing a comprehensive, real-time account of our journey to replicate the groundbreaking O1 model, we aim to:

+
    +
  • +

    Addressing the Challenges of Modern AI Research: The progress report tackles the growing complexity of AI research by offering real-time updates, enhancing transparency, and maintaining researcher motivation throughout prolonged, team-based projects.

    +
  • +
  • +

    Fostering Open Science and Collective Advancement: By sharing tools, datasets, methodologies, and setbacks, the report promotes knowledge sharing and accelerates progress across the research community, aligning with the principles of open science.

    +
  • +
  • +

    Laying the Foundation for AI in Scientific Discovery: Detailed documentation of the research process, including both successes and failures, provides a valuable dataset for training AI systems capable of understanding and contributing to scientific discovery, beyond mere outcomes.

    +
  • +
  • +

    Promoting Responsible AI Development: The report encourages transparency in AI research by documenting decision-making processes and ethical considerations, fostering trust and accountability in the development of AI technologies.

    +
  • +
+ + \ No newline at end of file diff --git a/docs/projects/o1_replicate/examples/index.html b/docs/projects/o1_replicate/examples/index.html new file mode 100644 index 0000000..176e3ee --- /dev/null +++ b/docs/projects/o1_replicate/examples/index.html @@ -0,0 +1,22 @@ + + + + + +Examples | Walnut Plan + + + + + +
Skip to main content
+ + \ No newline at end of file diff --git a/docs/projects/o1_replicate/exploration_journey/construct_longthought/index.html b/docs/projects/o1_replicate/exploration_journey/construct_longthought/index.html new file mode 100644 index 0000000..2951cbb --- /dev/null +++ b/docs/projects/o1_replicate/exploration_journey/construct_longthought/index.html @@ -0,0 +1,21 @@ + + + + + +How to Construct Long Thoughts? | Walnut Plan + + + + + +
Skip to main content

How to Construct Long Thoughts?

+

Constructing long thoughts with actions such as reflection and backtracking is a key element of journey learning. We have explored several approaches to achieve this.

+
    +
  • Attempt1: Tree Search with LLM and Reward: In this method, reasoning is modeled as a search on a tree, where the problem is the root, and each node represents a reasoning step. When incorrect paths are identified, the model backtracks to find the correct solution. A fine-grained reward model guides this process, incorporating errors into the reasoning chain.
  • +
  • Attempt2: Propose-Critique Loop: Attempt 1 constructs long thought by executing searches on the tree based on predefined rules, but this limits the freedom of actions like backtracking and reflection. Therefore, we allow the model to choose its current actions. We constructed a Propose-Critique Loop, where we pre-define some possible actions for the model (i.e., continue, backtracking, reflection, terminate) and let the model select actions to build the reasoning tree. If the tree does not reach the final answer, the model can be informed of this negative signal, guiding it to reflect and correct its approach.
  • +
  • Attempt3: Multi-Agent Approach: Building long thought on the foundation of a reasoning tree presents several challenges, including the presence of numerous ineffective nodes that do not contribute to constructing Long Thought, as well as issues of logical inconsistency caused by reasoning steps that do not depend on the reflection behavior. To address this, we designed an algorithm utilizing multi-agent debate, where one agent acts as the policy model, continuously reasoning, while another agent serves as the critique model, indicating whether the policy model should continue with the current reasoning or perform actions like backtracking. The two agents engage in ongoing dialogue, naturally constructing a long thought dataset when the correct answer is found.
  • +
  • Attempt4: Human Thought Process Annotation: By observing how humans solve reasoning problems—through reflection, backtracking, and revision—we can comprehensively document and model high-quality long thought processes that reflect human-like reasoning.
  • +
+ + \ No newline at end of file diff --git a/docs/projects/o1_replicate/exploration_journey/construct_policy_tree/index.html b/docs/projects/o1_replicate/exploration_journey/construct_policy_tree/index.html new file mode 100644 index 0000000..d7df59d --- /dev/null +++ b/docs/projects/o1_replicate/exploration_journey/construct_policy_tree/index.html @@ -0,0 +1,23 @@ + + + + + +How to Construct an On-policy Reasoning Tree? | Walnut Plan + + + + + +
Skip to main content

How to Construct an On-policy Reasoning Tree?

+

Constructing a reasoning tree requires a policy model that performs single-step reasoning. Starting from a problem as the root node, the model generates possible reasoning steps as child nodes, continuing iteratively until a maximum depth is reached or the correct answer is found.

+
    +
  • +

    Policy Model and Step Segmentation: To structure reasoning steps, we used the dataset from Abel, fine-tuning DeepSeekMath-7B-Base to create Abel-DSMath. This model generates reasoning steps clearly segmented by lines, which allows for controlled and precise stepwise reasoning.

    +
  • +
  • +

    Reward Model and Pruning: Generating a reasoning tree is computationally expensive. To address this, we implemented beam search to prune erroneous steps and improve efficiency. Two reward models were tested: math-shepherd and o1-mini. While math-shepherd scores each step’s probability of correctness, o1-mini offers more robust step-level rewards, directly indicating whether a reasoning step is correct. By selecting only the K highest-scoring steps in each iteration, we drastically reduce the number of generated steps, making the process more efficient.

    +
  • +
+ + \ No newline at end of file diff --git a/docs/projects/o1_replicate/exploration_journey/eval_trial/index.html b/docs/projects/o1_replicate/exploration_journey/eval_trial/index.html new file mode 100644 index 0000000..1bde895 --- /dev/null +++ b/docs/projects/o1_replicate/exploration_journey/eval_trial/index.html @@ -0,0 +1,17 @@ + + + + + +How to Evaluate our Trials? | Walnut Plan + + + + + +
Skip to main content

How to Evaluate our Trials?

+

A Visualization Platform

+

In addition to testing accuracy scores using specific evaluation metrics on benchmarks, manually reviewing actual cases is a crucial step in evaluating data and models. Therefore, to provide a more intuitive way to evaluate the model’s performance on specific problems, we build a visual data analysis platform using Streamlit. Specifically, our visualization platform includes the visualization of synthetic trees and their corresponding long thoughts as well as the output of the trained model. Furthermore, when visualizing results, we support detailed conditional filtering, such as filtering for correctly or incorrectly answered questions, or whether the output contains keywords indicating reflection or hesitation (e.g., “wait”). Additionally, we support comparison between different iterations of synthetic data and model outputs, which makes it highly intuitive and helps us easily validate whether the new round of data or models is effective.

+
Visualizing the constructed search tree through an interactive data analysis platform.
+ + \ No newline at end of file diff --git a/docs/projects/o1_replicate/exploration_journey/human_annotation/index.html b/docs/projects/o1_replicate/exploration_journey/human_annotation/index.html new file mode 100644 index 0000000..d233109 --- /dev/null +++ b/docs/projects/o1_replicate/exploration_journey/human_annotation/index.html @@ -0,0 +1,35 @@ + + + + + +What would be an effective annotation strategy for human-ai collaboration? | Walnut Plan + + + + + +
Skip to main content

What would be an effective annotation strategy for human-ai collaboration?

+

We have developed a human-AI pipeline that generates high-quality, long-form reasoning data based on the MATH dataset, following our “journey learning” paradigm. This pipeline expands human-annotated solutions from a few lines to thousands of tokens, using key techniques to ensure efficient annotation.

+
    +
  • +

    1. Complete Thought Process: It’s vital to document trials, reflections, associations, and corrections in reasoning. Even cognitive transitions not consciously recognized should be captured, as they are crucial for training large language models.

    +
  • +
  • +

    2. Explicit Common-Sense Explanations: To avoid hallucinations in LLMs, human annotations should include explicit explanations of common-sense knowledge, even if it seems obvious, ensuring that LLMs don’t misinterpret omitted information.

    +
  • +
+

Once the human annotations are completed, AI-driven processes take over. We use sophisticated prompts for data augmentation in the following ways:

+
    +
  • +

    Data Granularity: We break down the problem-solving process into smaller, more digestible steps to enhance understanding.

    +
  • +
  • +

    Gradual Reasoning: LLMs are prompted to pause and reflect, simulating how students think and process information.

    +
  • +
  • +

    Student-Explorer Perspective: The LLMs approach problem-solving with curiosity, thinking through it as if for the first time, encouraging critical engagement in the learning process.

    +
  • +
+ + \ No newline at end of file diff --git a/docs/projects/o1_replicate/exploration_journey/longthought_work/index.html b/docs/projects/o1_replicate/exploration_journey/longthought_work/index.html new file mode 100644 index 0000000..b2365a4 --- /dev/null +++ b/docs/projects/o1_replicate/exploration_journey/longthought_work/index.html @@ -0,0 +1,16 @@ + + + + + +How does Long Thought Work? | Walnut Plan + + + + + +
Skip to main content

How does Long Thought Work?

+

While we are still in the hypothesis stage without sufficient empirical evidence, we believe the success of O1’s long-thought approach is due to journey learning, as discussed earlier. Unlike shortcut learning, journey learning allows the model to explore the entire decision-making process, much like human problem-solving. O1 can consider multiple solution paths, learn from mistakes, and develop a deeper understanding of the problem—not just finding the correct answer but understanding why and how to reach it.

+

By navigating both correct and incorrect paths, O1 improves its error-handling and adaptability to new challenges. This trial-and-error process, combined with reflection and adjustment, mirrors human cognitive processes and enhances the model’s explainability. O1 can not only provide the correct solution but also explain the reasoning behind it, including how it recovers from errors. This thorough exploration is why O1 excels at handling complex problems and offering reliable, interpretable answers across a range of tasks.

+ + \ No newline at end of file diff --git a/docs/projects/o1_replicate/exploration_journey/o1_thought/index.html b/docs/projects/o1_replicate/exploration_journey/o1_thought/index.html new file mode 100644 index 0000000..60a0fd3 --- /dev/null +++ b/docs/projects/o1_replicate/exploration_journey/o1_thought/index.html @@ -0,0 +1,23 @@ + + + + + +What does O1’s Thought Look Like? | Walnut Plan + + + + + +
Skip to main content

What does O1’s Thought Look Like?

+

Official Example

+

The original example is the Math example from the OpenAI Blog.

+
The Thought Structure of OpenAI O1 in Mathematical Reasoning.
+

Analysis

+
ExampleToken CountLine CountAvg. Words per LineKeyword Count
Cipher89156684.29So: 31, First: 27, of: 27, Alternatively: 21, Second: 19, Third: 15, But: 15, Wait: 13, Alternatively perhaps: 13, Let: 12, and: 12, let: 11, first: 9, Now: 9, Think step: 8, step by: 8, by step: 8, So the: 8, the first: 8, Similarly: 6, as: 6, or: 5, we need to: 5, Option: 4, maybe: 4
Coding32591973.64and: 8, as: 7, then: 4, For: 4, Now: 4, So: 3, Let: 3, Since: 3, We: 3, row=: 3, step by: 2, by step: 2, We need: 2, Let me: 2, step by step: 2, We need to: 2, Let me try: 2
Crossword53113965.75Across: 37, So: 33, From: 31, and: 25, first: 19, Position: 19, we: 15, Now: 13, Possible: 13, as: 7, But: 7, Similarly: 7, third: 7, First: 6, So we: 6, Given: 5, Now let: 5, We: 4
English757499.88the: 20, that: 15, to: 15, is: 13, because: 8, why: 7, Option because: 5, and: 4
Health Science1010866.14and: 11, So: 5, Also: 3, But: 3, First: 2, Then: 2, So the: 2, but also: 2
Math187515219.49Therefore: 48, But: 42, So: 38, Thus: 36, Similarly: 33, we: 26, and: 17, since: 16, for: 15, real: 15, Wait: 14, Let: 10, but: 9, Let me: 9, all: 8, k1: 8, Given: 8, Wait but: 8, Alternatively: 7, we can: 7, So the: 7, Then: 6, Given that: 6
Safety510418.27So: 6, and: 65, But: 3, Also: 2, ChatGPT: 1, Write: 1, Explain: 1
Science2411917.62and: 14, can: 6, compute: 6, But: 5, So: 4, Now: 3, Given: 2, but: 2, so: 2, Alternatively: 2
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Statistical summary of various examples from OpenAI O1’s thought process across different domains. The table presents key metrics including the token count, the line count, the average number of words per line, and the frequency of the highest-occurring words or phrases derived using the n-gram algorithm. These keywords reflect the structure and style of the reasoning process, highlighting how the model introduces logical steps, alternatives, or corrections in different contexts.

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Our analysis focuses on O1’s reasoning examples provided by OpenAI, which includes eight instances of problem-solving steps for complex tasks. We categorized these examples by problem type and difficulty, observing that as problem difficulty increases, so does the length of the model’s responses in both tokens and lines. This trend suggests that more challenging problems require more reasoning steps.

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Beyond just response length, we analyzed keyword frequencies. Words like “consider,” “if,” and “possible” frequently appear in more complex problems, indicating the model is exploring multiple solution paths. Keywords such as “wait” and “alternatively” signal the model’s ability to reflect and self-correct, suggesting a deeper, non-linear reasoning process.

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Additionally, we manually reviewed O1’s approach to solving mathematical problems, revealing a methodical thought process. The model uses iterative problem-solving techniques, breaking down complex equations, and frequently reassessing its steps for consistency. It also tests multiple hypotheses and concludes by verifying its final solution, ensuring accuracy and reliability in its reasoning.

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Outline

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This section outlines the core of our O1 replication project, guiding readers through our research journey using key questions that reflect the complexity of the process. From our initial evaluation of O1 with the OlympicArena datasets to the construction of long thoughts, our work has involved numerous attempts, iterations, and in-depth analysis of O1’s capabilities.

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Each question in this chapter highlights a critical aspect of O1’s cognitive processes. We start by examining the structure of O1’s thoughts and progress to more advanced concepts, such as the development of reward models, on-policy reasoning trees, and how these elements come together in constructing long thoughts. Our methodology, as depicted in our research timeline, emphasizes iterative evaluation and training strategies, combining both quantitative assessments and human feedback.

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This question-driven format not only narrates our technical journey but also reflects the broader “journey learning” paradigm we follow, where the process of exploration is just as important as the final outcomes. By sharing our decision-making process, challenges, and innovative solutions, we hope to offer valuable insights to the AI community and encourage further advancements in AI research.

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Our journey—marked by persistence, collaboration, and innovation—demonstrates the potential of open research in tackling the challenges of replicating advanced AI models like O1.

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Our Research Journey

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This figure outlines our research journey exploring OpenAI's o1 technology from its release through October 8, 2024. A timeline tracks our progress chronologically, with research activities flowing vertically in the main diagram. Following the o1 release, we progressed from initial evaluation and knowledge acquisition to team assembly and analysis. Our exploration then focused on four long thought construction attempts: (1) Propose-Critique Loop, (2) Tree Search with LLM and Reward, (3) Multi-Agent Approach, and (4) Complete Human Thought Documentation. The second attempt, our core exploration, splits into three tracks: Process-Level Reward Model, Construction of Reasoning Tree, and Integrating Reasoning Tree into Long Thought. These converge in an iterative cycle of model improvement, including both quantitative and qualitative evaluation. The diagram's right side illustrates our training pipeline, featuring pre-training, iterative training, and optimization techniques. Solid black elements represent completed paths and milestones, while gray dashed elements indicate planned future explorations. This visualization captures both our achievements and future research directions in o1 technology development.
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How to Construct Reward Model?

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To build an effective reward model, the first step is determining the appropriate granularity. Rather than evaluating only final results, we focus on step-level granularity to enhance LLM capabilities in reflection and backtracking. Using fine-tuning data, we distinguish solutions by line numbers to capture more detailed cognitive processes.

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Meta-Evaluation

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We tested both open-source and proprietary reward models on subsets of the PRM800K and MR-GSM8K datasets, comparing their performance. The results, presented in our tables, show that O1-mini consistently performs best across different datasets.

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ModelF1 score
o1-mini0.855
GPT-4o-mini0.722
Math-shepherd0.734
ReasonEval-7B0.728
ReasonEval-34B0.735
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Results on the subset of MR-GSM8K

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ModelF1 score
GPT-4o-mini0.756
o1-mini0.880
o1-preview0.867
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Results on the subset of PRM800K

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How to Train our Models?

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Our experiments utilize the pre-trained language model deepseek-math-7b-base. The training process is divided into two main phases: Supervised Fine-Tuning (SFT) and Direct Preference Learning (DPO).

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Phase 1: Supervised Fine-Tuning (SFT)

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The SFT process consists of two stages:

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    ShortCut Learning: In this initial stage, we focus on fine-tuning the model using responses that include only the correct intermediate steps and the final correct answer. We fine-tune Deepseek-math-7b-base on the Abel dataset, which comprises 120k examples, and the PRM800K dataset. For each question in PRM800K, we utilize a single correct step-by-step solution, discarding responses that do not lead to the final answer. This results in a total of 6,998 examples for fine-tuning. During this stage, we conduct fine-tuning for one epoch on each dataset, primarily aiming to familiarize the model with the desired response format.

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    Journey Learning: In this second stage, we further fine-tune the initial stage SFT model using the long thoughts we constructed, which comprise 327 examples. This phase is designed to enhance the model's ability to detect errors, incorporate reflections, execute corrections, and perform backtracking. By training on long thoughts that include not only the correct reasoning paths but also erroneous trials, we aim to equip the model with a deeper understanding of the complexities involved in longer reasoning chains. As a comparison, we also fine-tune the model on the corresponding shortCut generated from the same reasoning tree, which also consists of 327 examples. Both the long thought SFT and shortCut SFT settings are trained for 3 epochs on these 327 examples.

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Phase 2: Direct Preference Learning (DPO)

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In this phase, we generate 20 responses per question from the MATH Train dataset, a re-divided dataset from PRM800k that includes 12,000 examples, using nucleus sampling with top_p = 0.95 and temperature T = 0.7. These 20 responses are categorized into positive and negative responses based on the correctness of the final answer. From these, we randomly select 5 positive responses and 5 negative responses to create 5 preference pairs. We then train the model using these preference pairs with DPO loss, allowing it to learn from the comparison of correct and incorrect answers.

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Results

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deepseek-sft-abeldeepseek-sft-prm800k
SFT-phase10.3720.290
SFT-phase2-shortcutLearning0.3860.348
SFT-phase2-journeyLearning0.4700.428
DPO0.4720.440
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Table: Training Results on MATH Test Set

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How to Derive a Long Thought from a Reasoning Tree?

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Once the reasoning tree is constructed, the next step is to derive a long thought that includes trial and error, moving beyond traditional shortcuts focused solely on the correct answer.

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    1. Constructing the Shortcut: We first construct the shortCut from the reasoning tree, which includes only the correct answer and valid intermediate steps. Starting from the root node, which represents a question, we identify a path that leads to a correct answer leaf node. If there are multiple correct answer nodes, multiple correct paths will be established.

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    2. raversal Path: To generate a long thought, we use Depth First Search (DFS) to explore the reasoning tree. The DFS explores both correct and incorrect paths, documenting each step and its reasoning. To simplify the process and reduce excessive exploration, we set constraints—allowing only a limited number of trials on incorrect paths for each node.

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    3. Long Thought Construction: After generating the traversal path, we compile a draft long thought that includes reasoning for both correct and incorrect steps. However, initial drafts produced suboptimal results, so we use GPT-4o to refine the draft, improving coherence while preserving the reflections, corrections, and reasoning steps. This results in a long thought that not only captures the complete problem-solving process but also flows naturally, simulating human-like reasoning.

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Future Plan

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As our O1 Replication Journey continues to evolve, our future plans are shaped by the insights gained and challenges encountered thus far. Drawing from our research timeline and the progress we've made, we've identified several key areas for future exploration and development:

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Scaling Up Long Thought Integration: Building on our successful iterations of long thought integration, we plan to conduct a third round of integration, as indicated in our research diagram. This will involve scaling up our processes to handle more complex and diverse thought patterns, potentially uncovering new dimensions of O1’s capabilities.

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Experiments on Long Thought Scaling Laws: Our diagram highlights planned experiments on long thought scaling laws. This research stream aims to understand how the performance and capabilities of our model scale with increases in data, model size, and computational resources. These insights will be crucial for optimizing our approach and potentially discovering fundamental principles underlying advanced AI systems.

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Fine-Grained, Thought-Centric Evaluation: We plan to develop and implement more sophisticated evaluation methodologies, focusing on fine-grained, thought-centric assessment. This approach, highlighted in our research timeline, will allow us to more accurately measure the quality and coherence of the generated long thoughts, providing deeper insights into our model’s reasoning capabilities.

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Human-AI Collaboration for Quality Thought: A key component of our future plan, as shown in the diagram, is to explore and enhance human-AI collaboration for producing high-quality thoughts. This involves developing interfaces and methodologies that leverage the strengths of both human intelligence and AI capabilities, potentially leading to breakthroughs in hybrid intelligence systems.

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Continued Improvement of Reward and Critique Models: Building on our process-level reward model and critique model setup, we aim to refine these systems further. This ongoing process will involve iterative improvements to better capture the nuances of human-like reasoning and problem-solving strategies.

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Advanced Integration of Reasoning Trees: We plan to explore more sophisticated methods of deriving and integrating long thoughts from our reasoning trees. This will involve developing advanced algorithms for traversing and synthesizing information from these complex structures.

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Expansion of Training Methodologies: Our future plans include further experimentation with and refinement of our training pipeline. This encompasses enhancements to our pre-training, iterative training, reinforcement learning, preference learning, and DPO (Direct Preference Optimization) stages, as outlined in our research diagram.

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Continued Transparency and Resource Sharing: In line with our commitment to open science, we will continue to share resources, insights, and tools developed throughout our journey. This ongoing practice, represented by the resource-sharing icons in our diagram, aims to foster collaboration and accelerate progress in the wider AI research community.

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Exploration of Multi-Agent Approaches: Building on our initial attempts with multi-agent systems, we plan to delve deeper into this area, potentially uncovering new ways to model complex reasoning and decision-making processes.

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Refinement of Analysis Tools: We aim to further develop and enhance our analysis tools, as indicated in our research timeline. These tools will be crucial for interpreting model outputs, tracking progress, and guiding future research directions.

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By pursuing these avenues, we aim to not only advance our understanding and replication of O1’s capabilities but also to push the boundaries of AI research methodologies. Our future plans reflect our commitment to the journey learning paradigm, emphasizing continuous improvement, transparent exploration, and collaborative advancement in the field of artificial intelligence. As we move forward, we remain adaptable to new discoveries and challenges, ready to adjust our plans as our understanding of O1 and advanced AI systems continues to evolve. Through this ongoing journey, we hope to contribute significantly to the development of more capable, interpretable, and ethically aligned AI systems.

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Journey Learning vs Shortcut Learning

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Many current machine learning and large language model approaches can be described as “shortcut learning.” This method focuses on achieving quick results by heavily relying on large amounts of data to improve performance. However, it often struggles with generalization, meaning it performs poorly in situations outside its training data, and lacks the ability to self-correct mistakes. While it has driven advancements, shortcut learning shows limitations in handling complex, dynamic, and open-ended challenges, making it less effective for developing truly intelligent AI.

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A paradigm shift from “shortcut learning” to “journey learning”. A searching tree for reasoning tasks. For the math problem-solving task, the root node represents the initial problem, while the leaf nodes are final conclusions. Green nodes indicate correct answers, and red nodes incorrect ones. Traditionally, learning focused on supervised training of a direct root-to-leaf shortcut path. This work, however, explores supervised learning of the entire exploration path, encompassing trial-and-error and correction processes.
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To address these limitations, we propose a new approach: “journey learning.” This paradigm goes beyond the traditional focus on specific tasks and emphasizes continuous progress through learning, reflection, and adaptation. AI systems that follow this method can evolve over time, improving their ability to handle real-world complexities. Unlike shortcut learning, journey learning equips AI with the capacity to adapt, backtrack, and refine its understanding, aiming to create more human-like intelligence.

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CharacteristicShortcut LearningJourney Learning
Learning DepthSurface features and simple correlationsDeep causal relationships and underlying principles
Reasoning AbilityLimited, struggles with complex reasoningPowerful, demonstrates human-like reasoning
Self-ImprovementLacks self-correction mechanismsContinuous self-assessment and improvement
GeneralizationLimited, easily affected by data distribution changesStrong, can handle new situations
Innovation CapacityLimited, struggles to solve new problemsHigh, can generate innovative solutions
Data DependencyHighly dependent on large training datasetsMore focused on quality and learning strategies
InterpretabilityPoor, often seen as a "black box"Better, can track internal reasoning processes
Ethical ConsiderationsMay unintentionally amplify data biasesEasier to implement ethical constraints and adjustments
SecurityVulnerable to adversarial attacksMore robust, able to identify potential threats
Long-term ValueQuick results in specific tasksPaves the way for AGI development
Human AnalogyExam-oriented education, crash coursesComprehensive education, lifelong learning
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Comparison between Shortcut Learning and Journey Learning.

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This shift to journey learning opens up new possibilities in AI research, enabling the creation of systems that can not only perform tasks but also reason and grow, making them more capable of engaging with humans across various domains.

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Overview

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The first project introduces a pioneering approach to artificial intelligence research, embodied in our O1 Replication Journey. In response to the announcement of OpenAI’s groundbreaking O1 model, we embark on a transparent, real-time exploration to replicate its capabilities while reimagining the process of conducting and communicating AI research.

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Paper:

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Github: https://github.com/GAIR-NLP/O1-Journey

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🚀 Advancing AI’s Reasoning Abilities

We strive to elevate AI’s reasoning to a level where it can think deeply, akin to great minds like Newton and Einstein, through open innovation and knowledge sharing.

🔬 AI-Driven Scientific Research

Our vision is to empower AI to drive scientific research, guiding and accelerating the expansion of human knowledge and fostering greater innovation.

🌍 Building a Collaborative AI Community

We aim to unite researchers and developers worldwide, fostering collaboration to drive the evolution of AI and create a more intelligent and open future.

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Key Recent Works

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Our team has a rich and diverse research background, with a strong focus on advancing core areas of AI and reasoning. We have extensively explored the domains of evaluation methodologies, including novel approaches for assessing reasoning beyond traditional accuracy metrics. Additionally, our research delves into AI alignment, particularly in fostering honesty and ethical behavior in models. We are also deeply involved in the development of datasets and benchmarks that challenge AI’s cognitive and reasoning abilities across multiple disciplines. Moreover, we have contributed significantly to generative AI for complex domains like mathematics, where we emphasize large-scale pretraining and reasoning. This solid foundation of prior work equips us with the expertise to tackle ambitious AI challenges and foster continued innovation in the field.

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Some highly relevant key recent projects are listed below (ordered by the time of publication on arXiv):

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ProjectFocusGitHubWebsitePublicationDate
Generative AI for Math: AbelReasoning modelsCodeHomepageArXiv2023.09
Alignment for HonestyHonesty-aligned modelsCodeHomepageNeurIPS 20242023.12
MathPile: A Billion-Token-Scale Pretraining Corpus for MathMath pre-train dataCodeHomepageNeurIPS 20242023.12
Reformatted AlignmentAlignment methodsCodeHomepageEMNLP 20242024.02
Evaluating Mathematical Reasoning Beyond AccuracyReasoning EvaluationCode-ArXiv2024.04
OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AIReasoning EvaluationCodeHomepageNeurIPS 20242024.06
Progress or Regress? Self-Improvement Reversal in Post-trainingEvaluationCode-ICML 2024 workshop2024.07
Weak-to-Strong ReasoningReasoningCode-EMNLP 20242024.07
OpenResearcher: Unleashing AI for Accelerated Scientific ResearchAI for scientific researchCode-EMNLP 2024 demo2024.08
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Markdown page example

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You don't need React to write simple standalone pages.

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