AEM: Adaptive Entropy Modulation for Multi-Turn Agentic Reinforcement Learning

Researchers present AEM (Adaptive Entropy Modulation), a new credit assignment method for reinforcement learning that improves how language model agents learn from sparse rewards without requiring dense supervision. The technique adaptively modulates entropy during training to balance exploration and exploitation, achieving a 1.4% improvement on the challenging SWE-bench-Verified benchmark across models ranging from 1.5B to 32B parameters.

AEM addresses a fundamental challenge in training reinforcement learning agents: efficiently learning from sparse, outcome-only rewards where assigning credit to individual steps in a multi-turn task is computationally and conceptually difficult. Traditional solutions rely on dense intermediate supervision through process reward models or self-supervised signals, which increase complexity and often fail to generalize across different domains. The proposed method eliminates this supervision requirement by mathematically modulating entropy dynamics during training, creating a natural transition from exploration to exploitation phases.

The theoretical contribution elevates entropy analysis from the token level to the response level, reducing sampling variance and deriving a practical proxy that reshapes training dynamics. By showing that entropy drift under natural gradients is governed by advantage and relative response surprisal, the researchers provide a principled approach to credit assignment. This theoretical grounding distinguishes AEM from heuristic solutions and suggests broader applicability beyond the tested benchmarks.

The experimental validation demonstrates consistent improvements across multiple model scales and benchmarks, with the 1.4% gain on SWE-bench-Verified—a highly challenging software engineering benchmark—suggesting the method handles complex, real-world tasks effectively. This matters for developers and AI practitioners seeking more efficient training methods that reduce annotation overhead and computational costs. As LLM agents increasingly tackle multi-step reasoning tasks in production systems, more efficient credit assignment translates directly to lower training costs and faster iteration cycles.

The work signals momentum in making RL training for language models more practical and accessible. Future implementations may integrate AEM into existing training pipelines, and the supervision-free nature could enable broader experimentation with RL-based agent training across diverse domains without prohibitive annotation requirements.

• →AEM eliminates the need for dense supervision in RL training by adaptively modulating entropy, reducing annotation and tuning complexity.
• →The method achieves 1.4% improvement on SWE-bench-Verified, demonstrating effectiveness on challenging real-world tasks.
• →Theoretical contribution elevates entropy analysis from token to response level, providing a principled mathematical framework for credit assignment.
• →Supervision-free approach scales effectively across model sizes from 1.5B to 32B parameters.
• →Method enables natural transition from exploration to exploitation phases without manual tuning, improving training efficiency.