Where Do CoT Training Gains Land in LLM based Agents?

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Jingyu Liu, Zhiwen Wang, Yuxin Jing, Huanyu Zhou, Yong Liu
• Venue: arXiv: cs.AI
• Published: 2026-06-25
• Collected: 2026-06-26
• URL: http://arxiv.org/abs/2606.26935v1
• PDF: https://arxiv.org/pdf/2606.26935v1
• DOI: UNKNOWN

Plain-Language Brief

The research investigates the role of Chain-of-Thought (CoT) reasoning in language model agents, finding that while CoT training improves the quality of actions predicted by the model, it does not significantly widen the advantage of CoT over direct action prediction. Later checkpoints show less tendency to revise actions based on CoT, implying a greater reliance on the prompt. The study also suggests that selectively masking action-token supervision during training can improve out-of-domain generalization.

What the Document Says

• The paper studies the improvement brought by Chain-of-Thought (CoT) training in language model agents.
• The authors find that the quality of actions predicted without CoT (prompt actions) improves substantially across checkpoints.
• During interaction with the environment, the relative advantage of CoT actions over prompt actions remains similar.
• The study suggests that CoT training does not widen the advantage of CoT reasoning.
• CoT training helps 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.
• Masking action-token supervision during training improves out-of-domain generalization.

Key Concepts Mentioned

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

Claims Made by the Document

• CoT training improves the quality of actions predicted by language model agents.
• The relative advantage of CoT actions over prompt actions remains similar during interaction with the environment.
• Later checkpoints show less tendency to revise actions based on CoT, implying greater reliance on the prompt.
• Masking action-token supervision during training improves out-of-domain generalization.

Evidence Used

• Improvement in quality of prompt actions across checkpoints.
• Similar relative advantage of CoT actions and prompt actions during interaction with the environment.
• Less tendency to revise actions based on CoT in later checkpoints.
• Improved out-of-domain generalization after masking action-token supervision.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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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.