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Source note3 min read

Where Do CoT Training Gains Land in LLM based Agents?

Source

arXiv

arxiv.org/abs/2606.26935v1

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Source

Plain-Language Brief

The study investigates how Chain-of-thought (CoT) training in language model agents affects their performance. The authors find that while CoT improves overall, it doesn't significantly widen the advantage of CoT reasoning over direct prediction from prompts. Later checkpoints rely more on the prompt and less on revising actions based on CoT. A selective masking of action-token supervision during training enhances out-of-domain generalization.

What the Document Says

  • Chain-of-thought (CoT) reasoning in language model agents is studied.
  • Prior work shows that verbalized CoT may not always be faithful and could reflect post-hoc reasoning.
  • The question is if CoT training improves the model's ability to change actions through generated reasoning or predict actions directly from prompts.
  • Comparisons are made between prompt-actions (predicting action without CoT) and CoT actions (predicting action with CoT).
  • Across checkpoints, prompt-action quality improves significantly.
  • In an environment interaction, the relative advantage of CoT actions over prompt actions remains consistent.
  • CoT training does not widen the advantage of CoT reasoning and helps to improve the quality of prompt actions.
  • Later checkpoints are less likely to revise the action in response to CoT, indicating greater reliance on the prompt.
  • A selective masking of action-token supervision during training improves out-of-domain generalization.

Key Concepts Mentioned

  • Chain-of-thought reasoning
  • language model agents
  • prompt actions
  • CoT actions
  • checkpoints
  • environment interaction
  • out-of-domain generalization

Claims Made by the Document

  • CoT training does not significantly widen the advantage of CoT reasoning over direct prediction from prompts.
  • Later checkpoints rely more on the prompt and less on revising actions based on CoT.
  • A selective masking of action-token supervision during training improves out-of-domain generalization.

Evidence Used

  • Improvement in prompt-action quality across checkpoints
  • Consistency in the relative advantage of CoT actions over prompt actions
  • Patterns indicating greater reliance on the prompt and less action revision in later checkpoints
  • Improved out-of-domain generalization after masking action-token supervision

Methods / Approach

  • Comparing prompt-actions with CoT actions
  • Environment interaction
  • Selective masking of action-token supervision during training

Limitations Stated by the Authors

Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

The study does not provide insights into the mechanisms behind these patterns or explain why later checkpoints rely more on prompts.

Open Questions

  • What are the mechanisms behind the observed patterns?
  • Why do later checkpoints rely more on prompts?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The authors investigate how CoT training in language model agents affects their performance. They find that while CoT improves overall, it doesn't significantly widen the advantage of CoT reasoning over direct prediction from prompts. Later checkpoints rely more on the prompt and less on revising actions based on CoT. A selective masking of action-token supervision during training improves out-of-domain generalization. However, the study does not provide insights into the mechanisms behind these patterns or explain why later checkpoints rely more on prompts.

Raw Abstract / Excerpt

Chain-of-thought (CoT) reasoning is widely used in language-model agents, but prior work has shown that verbalized CoT is not always faithful and may instead reflect post-hoc reasoning, which means the model already knows the answer before reasoning. We therefore ask what CoT training is actually improving: is the model getting better at changing its action through generated reasoning, or is it getting better at predicting the action directly from the prompt? We study this question by comparing \emph{prompt actions} (predicting action without CoT) with CoT actions (predicting action with CoT). Across checkpoints, prompt-action quality improves substantially. While interacting with the environment, the relative advantage of CoT actions over prompt actions remains similar, showing that CoT training does not widen the advantage of CoT reasoning, and it helps to improve the quality of prompt actions. We further find that later checkpoints are less likely to revise the action in response to CoT, suggesting greater reliance on the prompt. Motivated by these patterns, we selectively mask action-token supervision on a fraction of training examples. This intervention improves out-of-domain generalization.