Where Do CoT Training Gains Land in LLM based Agents?
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 env...
| Authors | Jingyu Liu, Zhiwen Wang, Yuxin Jing et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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