diff --git a/src/liger_kernel/chunked_loss/kto_loss.py b/src/liger_kernel/chunked_loss/kto_loss.py
index b0f6886b8..59d59cbf9 100644
--- a/src/liger_kernel/chunked_loss/kto_loss.py
+++ b/src/liger_kernel/chunked_loss/kto_loss.py
@@ -18,15 +18,32 @@ def preference_loss_fn(
         beta=0.1,
     ):
         """
-        Paper: https://arxiv.org/abs/2402.01306
+        Implements the Kahneman-Tversky Optimization (KTO) loss function.
+        Paper: "KTO: Model Alignment as Prospect Theory-Guided Optimization"
+        https://arxiv.org/abs/2402.01306
+
+        KTO loss is inspired by prospect theory (https://en.wikipedia.org/wiki/Prospect_theory)
+        from behavioral economics, which models how humans make decisions under uncertainty.
+        The loss function is asymmetric, treating gains and losses differently, similar to
+        human decision-making patterns.
 
         Formula:
+        When y is chosen:
         L_KTO = 1 - σ(β * (log[π(x)/π₀(x)] - KL(π||π₀)_y))
+        When y is rejected:
+        L_KTO = 1 - σ(β * (KL(π||π₀)_y - log[π(x)/π₀(x)]))
 
         Where:
         - σ: Sigmoid function
-        - β: Temperature parameter
-        - KL(π||π₀)_y is KL divergence estimated using the rejected response y
+        - β: Temperature parameter controlling the strength of the preference signal
+        - π(x): Policy (current model)
+        - π₀(x): Reference policy (reference model)
+        - KL(π||π₀)_y: KL divergence estimated using the rejected response y
+
+        The loss encourages the model to:
+        1. Assign higher probability to chosen responses
+        2. Assign lower probability to rejected responses
+        3. Maintain reasonable distance from the reference model
 
         Args:
             chosen_logps: Log probabilities of chosen tokens (batch_size,)
@@ -35,6 +52,12 @@ def preference_loss_fn(
             ref_chosen_logps: Reference log probs of chosen tokens (batch_size,)
             ref_rejected_logps: Reference log probs of rejected tokens (batch_size,)
             beta: Weight for the direct preference loss
+
+        Returns:
+            Tuple of (loss, chosen_rewards, rejected_rewards):
+            - loss: The KTO loss value
+            - chosen_rewards: Reward signals for chosen responses (detached)
+            - rejected_rewards: Reward signals for rejected responses (detached)
         """
         if ref_chosen_logps is None:
             ref_chosen_logps = torch.tensor(0.0, device=chosen_logps.device)
@@ -60,7 +83,8 @@ def preference_loss_fn(
         rejected_rewards = beta * rejected_logratios.detach()
 
         return (
-            losses.sum() / (full_target.shape[0] // 2),
+            # We don't divide by 2 because KTO Loss doesn't need pair-wise examples
+            losses.sum() / (full_target.shape[0]),
             chosen_rewards,
             rejected_rewards,
         )