Effects of policy entropy on agentic RL training

Characterize how policy entropy affects training effectiveness, stability, and final performance in GRPO-based agentic reinforcement learning where language model agents interleave tool calls with internal reasoning.

Background

Conflicting prescriptions exist in the literature: some advocate entropy minimization for determinism, while others emphasize high-entropy tokens to avoid early collapse. Tool-call steps may induce useful uncertainty in agentic RL.

The authors stress that the general role of entropy in agentic RL remains unclear, motivating experiments on clipping and entropy regimes to identify balanced exploration that avoids instability without premature convergence.

References

For agentic RL, however, it remains unclear (i) what techniques work best for policy optimization, (ii) what is the relationship between the exploration(pass@k)-exploitation(average@k), and (iii) how does entropy affect training effectiveness, stability, and final performance.

— Demystifying Reinforcement Learning in Agentic Reasoning  (2510.11701 - Yu et al., 13 Oct 2025) in Section 4 (Algorithmic Design and Training Dynamics in Agentic RL), opening paragraph

Identifying the cause is an interesting open research direction.

— PhantomEnvironments: Training LLM Agents in Fictional Worlds  (2609.40221 - Kabra et al., 30 Sep 2026) in Appendix, Section 'RL Fine-tuning', paragraph 'Training dynamics and instability'

Whether the relationship generalizes across model families, adaptation horizons, and broader task distributions remains open.

— Sharpen Before You Adapt: Data-Free Entry-State Sharpening for Test-Time Reinforcement Learning  (2610.00903 - Zhang et al., 1 Oct 2026) in Section 6, Limitations