diff --git a/src/levanter/optim/config.py b/src/levanter/optim/config.py
index 6d61159bd..c6b3bd783 100644
--- a/src/levanter/optim/config.py
+++ b/src/levanter/optim/config.py
@@ -24,7 +24,7 @@ class OptimizerConfig(draccus.ChoiceRegistry, abc.ABC):
 
     min_lr_ratio: float = 0.1
     warmup_ratio: Optional[float] = None  # Deprecated. fraction of training steps to use as warmup
-    """The lr scheduler operates on 4 stages: [warmup] - [stable] - [decay] - [cooldown]"""
+    """The lr scheduler operates on 4 stages: [warmup] - {[stable] - [decay]} x haps - [cooldown]"""
     warmup: float = 0.01
     """fraction of training steps to use as warmup, or steps to use. 0.0 means no warmup"""
     stable: float = 0.00
@@ -32,6 +32,8 @@ class OptimizerConfig(draccus.ChoiceRegistry, abc.ABC):
     cooldown: float = 0.0
     """fraction of training steps to use as cooldown, or steps to use. 0.0 means no cooldown"""
     lr_schedule: str = "cosine"  # constant, cosine, linear
+    haps: Optional[list[int]] = None
+    """list of integers indicating pit stop steps. See paper https://openreview.net/pdf?id=RSsavSvAvN"""
     weight_decay_modules: Optional[list[str] | str] = None
     """A regex or a list of strings to identify where to mask weight.
     For nano-GPT, this field can be set as `r".*attn.*weight|.*mlp.*weight|.*token_embeddings|.*position_embeddings"`"""
@@ -138,22 +140,13 @@ def mask_fn(model):
 
     def lr_scheduler(self, num_train_steps):
         warmup_steps = self._convert_warmup(num_train_steps)
-        stable_steps = _convert_ratio_or_steps(self.stable, num_train_steps)
         cooldown_steps = _convert_ratio_or_steps(self.cooldown, num_train_steps)
-        lr_decay_steps = num_train_steps - warmup_steps - stable_steps - cooldown_steps
-        min_lr = self.learning_rate * self.min_lr_ratio
+        if self.haps is None:
+            self.haps = []
+        self.haps.insert(0, warmup_steps)
+        self.haps.append(num_train_steps - cooldown_steps)
 
-        match self.lr_schedule:
-            case "constant":
-                schedule = optax.constant_schedule(self.learning_rate)
-            case "cosine":
-                schedule = optax.cosine_decay_schedule(self.learning_rate, lr_decay_steps, self.min_lr_ratio)
-            case "linear":
-                schedule = optax.linear_schedule(self.learning_rate, min_lr, lr_decay_steps)
-            case "inv_sqrt":
-                schedule = _inv_sqrt_decay_schedule(self.learning_rate, min_lr, warmup_steps, 10000)
-            case _:
-                raise ValueError(f"Unknown lr_schedule: {self.lr_schedule}")
+        min_lr = self.learning_rate * self.min_lr_ratio
 
         schedules = []
         boundaries = []
@@ -163,18 +156,37 @@ def lr_scheduler(self, num_train_steps):
             schedules.append(warmup)
             boundaries.append(warmup_steps)
 
-        if stable_steps != 0:
-            stable = optax.constant_schedule(self.learning_rate)
-            schedules.append(stable)
-            boundaries.append(warmup_steps + stable_steps)
-
-        schedules.append(schedule)
+        for start, end in zip(self.haps[:-1], self.haps[1:]):
+            cycle_steps = end - start
+            stable_steps = _convert_ratio_or_steps(self.stable, cycle_steps)
+            lr_decay_steps = cycle_steps - stable_steps
+
+            if stable_steps != 0:
+                stable = optax.constant_schedule(self.learning_rate)
+                schedules.append(stable)
+                boundaries.append(start + stable_steps)
+
+            match self.lr_schedule:
+                case "constant":
+                    schedule = optax.constant_schedule(self.learning_rate)
+                case "cosine":
+                    schedule = optax.cosine_decay_schedule(self.learning_rate, lr_decay_steps, self.min_lr_ratio)
+                case "linear":
+                    schedule = optax.linear_schedule(self.learning_rate, min_lr, lr_decay_steps)
+                case "inv_sqrt":
+                    schedule = _inv_sqrt_decay_schedule(self.learning_rate, min_lr, warmup_steps, 10000)
+                case "inv":
+                    schedule = _inv_decay_schedule(self.learning_rate, min_lr, lr_decay_steps)
+                case _:
+                    raise ValueError(f"Unknown lr_schedule: {self.lr_schedule}")
+
+            schedules.append(schedule)
+            boundaries.append(end)
 
         if cooldown_steps != 0:
             final_main_lr = schedule(lr_decay_steps)
             cooldown = optax.linear_schedule(final_main_lr, min_lr, cooldown_steps)
             schedules.append(cooldown)
-            boundaries.append(num_train_steps - cooldown_steps)
 
         if len(schedules) > 1:
             schedule = optax.join_schedules(schedules, boundaries)
@@ -197,6 +209,14 @@ def schedule(count):
     return schedule
 
 
+def _inv_decay_schedule(lr: float, min_lr: float, decay_steps: int):
+    def schedule(count):
+        decay = jnp.minimum(1.0, 1.0 / ((lr / min_lr - 1) * jnp.maximum(count, 1) / decay_steps + 1))
+        return jnp.maximum(lr * decay, min_lr)
+
+    return schedule
+
+
 def _convert_ratio_or_steps(ratio_or_steps: float, num_train_steps: int):
     if ratio_or_steps < 1.0:
         return int(ratio_or_steps * num_train_steps)