Generalization of low-entropy tool-call token behavior beyond coding tools

Determine whether the observed low-entropy behavior of tokens corresponding to Python coding tool calls during agentic reinforcement learning generalizes to interactions with non-coding tools, and characterize the extent and conditions of such generalization.

Background

In the paper’s analysis of reasoning trajectories, the authors study token-entropy patterns during agentic rollouts. They observe that high-entropy tokens often correspond to exploratory or reflective decisions (“forking tokens”) and to reflections on tool responses, both of which contribute to improved reasoning.

They also report a distinct phenomenon: tokens associated with Python coding tool calls (code and comments) tend to be low-entropy, likely because the base model has been extensively pretrained on code. The authors explicitly state that it remains unknown whether this low-entropy behavior also appears when using non-coding tools, posing an open question about the generality of the entropy pattern across different tool modalities.

References

Another interesting observation is that coding tool call tokens themselves, which include Python code and code comments, are usually low-entropy. A likely explanation is that the pre-trained model has already been extensively trained on a large corpus of Python code. How this phenomenon generalizes to other non-coding tools remains an open question for future work.

— rStar2-Agent: Agentic Reasoning Technical Report  (2508.20722 - Shang et al., 28 Aug 2025) in Section: Analysis of Agentic Reasoning Behaviors (final paragraph)