Agents, Think Only When it Helps

Authors: Min-Seon Kim , Seungdong Yoa , Sanghyu Yoon , Ye Seul Sim , Suhee Yoon , Dongmin Kim , Jung-Eun Kim

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Abstract: LLM agent distillation transfers agentic capabilities to student models by training on complete teacher trajectories that typically interleave thoughts and actions. We question how critical thought supervision is for agents' ultimate action performance. To answer this, we disentangle their impacts by independently perturbing teacher thoughts and actions during distillation. On widely adopted agent benchmarks across model scales, altering or discarding thoughts has little effect on student performance. We find, however, that thought supervision becomes important when information about the thought is not disclosed in the corresponding action during training. By identifying such discrepant cases, our findings challenge the common practice of distilling entire teacher trajectories for LLM agents, and offer a new insight that thought supervision can be a discernible choice for efficiency depending on whether the information of thought is disclosed in the action.

Subjects:

Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG)

Cite as:

arXiv:2611.05709 [cs.AI]

(or

arXiv:2611.05709v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.05709

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jung-Eun Kim [ view email ]

[v1]

Tue, 3 Nov 2026 22:35:21 UTC (70 KB)

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