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arxiv: 2606.26935 · v1 · pith:4UPK5LOZnew · submitted 2026-06-25 · 💻 cs.AI

Where Do CoT Training Gains Land in LLM based Agents?

Jingyu Liu , Zhiwen Wang , Yuxin Jing , Huanyu Zhou , Yong Liu This is my paper

Pith reviewed 2026-06-26 04:52 UTC · model grok-4.3

classification 💻 cs.AI
keywords chain-of-thoughtLLM agentsprompt actionstraining gainsaction predictionout-of-domain generalizationreasoning faithfulness
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The pith

CoT training improves LLM agents mainly by raising the quality of direct prompt-to-action predictions rather than widening the extra benefit from reasoning.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper asks whether chain-of-thought training makes agents better at using generated reasoning to change their actions or simply better at guessing the right action straight from the prompt. By tracking prompt-only actions and CoT actions across training checkpoints and during environment interaction, it finds that prompt-action quality rises substantially while the relative edge of CoT actions stays roughly constant. Later checkpoints also become less likely to change their answer after generating CoT, pointing to greater reliance on the prompt itself. Motivated by these patterns, the authors mask action-token supervision on part of the training data and observe better out-of-domain generalization.

Core claim

Across training checkpoints, prompt-action quality improves substantially while the relative advantage of CoT actions over prompt actions remains similar during environment interaction; later checkpoints revise actions less often in response to CoT, indicating greater prompt reliance, and selectively masking action-token supervision on a fraction of examples improves out-of-domain generalization.

What carries the argument

The side-by-side comparison of prompt actions (direct prediction without generated reasoning) versus CoT actions (prediction after verbalized reasoning), measured across checkpoints and interaction steps.

If this is right

  • Prompt-action quality rises substantially with continued CoT training.
  • The relative advantage of CoT actions over prompt actions stays roughly constant during environment interaction.
  • Later checkpoints revise their initial action less often after generating CoT.
  • Masking action-token supervision on a fraction of training examples improves out-of-domain generalization.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The pattern suggests CoT training may function more as a way to strengthen base prompt-to-action mapping than as training for the reasoning step itself.
  • If the same separation holds in other agent benchmarks, selective supervision masking could become a standard regularization step for better generalization.
  • The reduced revision rate in later checkpoints raises the question of whether faithfulness of CoT decreases as direct prediction improves.

Load-bearing premise

Performance differences between prompt actions and CoT actions can be attributed cleanly to the presence or absence of generated reasoning rather than to how the model was trained or how the outputs were sampled.

What would settle it

If the gap between CoT-action and prompt-action success rates widened steadily across later checkpoints while interacting with the environment, the claim that training gains do not widen the CoT advantage would be contradicted.

Figures

Figures reproduced from arXiv: 2606.26935 by Huanyu Zhou, Jingyu Liu, Yong Liu, Yuxin Jing, Zhiwen Wang.

Figure 1
Figure 1. Figure 1: Alignment between prompt/CoT actions and the reference action. On the validation set, both prompt-action [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Prompt/CoT-action consistency during training. Each panel corresponds to one environment; colors [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Online evaluation of how well actions can be predicted from the prompt on unseen tasks. Panel (a) [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Checkpoint-wise consistency under perturbed reasoning traces in ALFWorld. Each panel corresponds to [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Prompt versus CoT attention during action [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Mean evaluation score on OOD tasks under vanilla CoT supervision ( [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: CoT-minus-prompt action gap under reduced action supervision across checkpoints. Each panel corre [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Consistency with the prompt action under [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Prompt/CoT-action consistency in MATH, MedQA, and GPQA GPQA), where prompts are much shorter and do not carry rich interaction histories. As shown in [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Prompt-action self-consistency conditioned on whether the prompt action matches or mismatches the [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Checkpoint-wise consistency under perturbed reasoning traces in BFCL. Increasing agreement with the [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Checkpoint-wise consistency under perturbed reasoning traces in ScienceWorld. Increasing agreement [PITH_FULL_IMAGE:figures/full_fig_p014_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Prompt/CoT-action consistency during reinforcement learning. Each panel corresponds to one environ [PITH_FULL_IMAGE:figures/full_fig_p014_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Checkpoint-wise prompt-action consistency under perturbed reasoning traces during reinforcement [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Checkpoint-wise prompt-action consistency under perturbed reasoning traces for the Llama model. [PITH_FULL_IMAGE:figures/full_fig_p016_15.png] view at source ↗
Figure 17
Figure 17. Figure 17: Prompt/CoT-action success gap in Science [PITH_FULL_IMAGE:figures/full_fig_p016_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Success gap between CoT actions and prompt actions under reinforcement learning (GRPO). [PITH_FULL_IMAGE:figures/full_fig_p017_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Mean evaluation score on in-domain tasks under vanilla CoT supervision ( [PITH_FULL_IMAGE:figures/full_fig_p017_19.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper investigates where gains from Chain-of-Thought (CoT) training accrue in LLM agents by comparing prompt actions (direct action prediction without generated reasoning) against CoT actions across training checkpoints. It reports that prompt-action quality improves substantially while the relative advantage of CoT actions stays roughly constant, that later checkpoints revise actions less often in response to CoT, and that selectively masking action-token supervision on a fraction of examples improves out-of-domain generalization.

Significance. If the empirical patterns hold after controls for sampling and elicitation, the work clarifies that CoT training primarily strengthens direct prompt-to-action mapping rather than widening the benefit of verbalized reasoning, and supplies a simple masking intervention with measurable OOD gains. The direct checkpoint-wise comparison is a strength that avoids post-hoc parameter fitting.

major comments (3)
  1. [§3] §3 (Methods) and abstract: the central claim that CoT training improves prompt-action quality while leaving the relative CoT advantage unchanged requires that prompt-action and CoT-action outputs differ only in the presence/absence of reasoning. The manuscript must specify exactly how prompt actions are elicited (modified prompt, token suppression, or temperature change) and confirm that the elicitation procedure is held fixed across checkpoints; without this, measured prompt-action gains could arise from changes in decoding behavior outside the CoT training distribution rather than improved direct prediction.
  2. [§4] §4 (Results) and abstract: no numerical values, standard errors, or statistical tests are supplied for the reported improvements in prompt-action quality or the stability of the CoT advantage. Checkpoint selection criteria, environment statistics, number of episodes, and temperature controls are also omitted; these details are load-bearing for the claim that the relative advantage “remains similar.”
  3. [§5] §5 (Intervention): the selective masking experiment is presented as motivated by the observed patterns, yet the manuscript does not report the fraction of examples masked, the precise masking schedule, or an ablation against random masking; without these, it is unclear whether the reported OOD gain is specific to the hypothesized mechanism.
minor comments (2)
  1. Figure captions and axis labels should explicitly state whether error bars represent standard error across seeds or across episodes.
  2. The term “prompt action” is used before it is formally defined; add a short definition in the introduction or §2.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We appreciate the referee's careful review and constructive suggestions. We address each of the major comments below and will incorporate the necessary revisions to improve the clarity and completeness of the manuscript.

read point-by-point responses

  1. Referee: [§3] §3 (Methods) and abstract: the central claim that CoT training improves prompt-action quality while leaving the relative CoT advantage unchanged requires that prompt-action and CoT-action outputs differ only in the presence/absence of reasoning. The manuscript must specify exactly how prompt actions are elicited (modified prompt, token suppression, or temperature change) and confirm that the elicitation procedure is held fixed across checkpoints; without this, measured prompt-action gains could arise from changes in decoding behavior outside the CoT training distribution rather than improved direct prediction.

    Authors: We thank the referee for highlighting this important clarification. The prompt actions are elicited by using a prompt template that does not include the instruction to generate chain-of-thought reasoning, while all other aspects of the input and decoding parameters remain unchanged. This elicitation method is applied consistently across all training checkpoints. We will update §3 and the abstract to explicitly describe this procedure. revision: yes

  2. Referee: [§4] §4 (Results) and abstract: no numerical values, standard errors, or statistical tests are supplied for the reported improvements in prompt-action quality or the stability of the CoT advantage. Checkpoint selection criteria, environment statistics, number of episodes, and temperature controls are also omitted; these details are load-bearing for the claim that the relative advantage “remains similar.”

    Authors: We agree that providing quantitative details and statistical support is necessary. In the revised manuscript, we will include the specific numerical improvements observed in prompt-action quality along with standard errors and appropriate statistical tests. We will also specify the checkpoint selection criteria, the number of evaluation episodes, environment statistics, and confirm that temperature is held fixed. These additions will substantiate the claim regarding the stability of the CoT advantage. revision: yes

  3. Referee: [§5] §5 (Intervention): the selective masking experiment is presented as motivated by the observed patterns, yet the manuscript does not report the fraction of examples masked, the precise masking schedule, or an ablation against random masking; without these, it is unclear whether the reported OOD gain is specific to the hypothesized mechanism.

    Authors: We will revise §5 to report the exact fraction of training examples on which action-token supervision was masked, describe the masking schedule in detail, and include an ablation study comparing the selective masking to random masking. This will clarify that the out-of-domain generalization gains are attributable to the hypothesized mechanism. revision: yes

Circularity Check

0 steps flagged

No circularity; claims rest on direct empirical comparisons

full rationale

The paper reports observational measurements of prompt-action quality versus CoT-action quality across training checkpoints, with the relative advantage remaining stable. These are direct comparisons of output modes on held-out interactions, not derivations, fitted parameters renamed as predictions, or self-citation chains. No equations appear that reduce any reported advantage to a quantity defined inside the paper, and the masking intervention is presented as an empirical follow-up motivated by the observations rather than a mathematical necessity. The central claims therefore remain independent of the inputs they analyze.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work is an empirical measurement study on existing LLM training; it relies on standard supervised fine-tuning assumptions and the validity of the prompt-vs-CoT comparison protocol. No new physical or mathematical entities are introduced.

axioms (2)
  • standard math Standard assumptions of supervised fine-tuning on next-token prediction apply to the agent training runs.
    Implicit in the use of training checkpoints and action-token supervision.
  • domain assumption The difference between prompt-action and CoT-action outputs isolates the contribution of generated reasoning.
    Central to interpreting the flat relative advantage as evidence that reasoning itself is not improving.

pith-pipeline@v0.9.1-grok · 5736 in / 1336 out tokens · 33799 ms · 2026-06-26T04:52:46.963588+00:00 · methodology

discussion (0)

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Reference graph

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