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

2026-05-04 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

AEM (Adaptive Entropy Modulation) is a novel, supervision-free credit assignment method designed to enhance multi-turn agentic reinforcement learning (RL) for large language models (LLMs). It addresses the challenge of sparse, outcome-only rewards by adaptively modulating entropy dynamics during training, thereby optimizing the exploration-exploitation trade-off. AEM elevates entropy analysis from the token-level to the response-level, proving that entropy drift under natural gradients is intrinsically controlled by the product of advantage and relative response surprisal. This method derives a practical proxy to reshape training dynamics, facilitating a natural transition from exploration to exploitation. Extensive experiments across ALFWorld, WebShop, and SWE-bench-Verified benchmarks, using models from 1.5B to 32B parameters, demonstrate AEM's effectiveness, including a notable +1.4% gain on the challenging SWE-bench-Verified benchmark when integrated with a state-of-the-art baseline.

Key takeaway

Research scientists developing multi-turn LLM agents should consider integrating AEM into their RL frameworks. By adaptively modulating response-level advantages based on entropy, you can mitigate premature entropy collapse, promote more effective exploration in early training, and achieve superior final performance without requiring additional supervision or significant computational overhead. This approach offers a robust way to improve credit assignment and optimize the exploration-exploitation balance in complex agentic RL scenarios.

Key insights

AEM uses response-level entropy to adaptively balance exploration and exploitation in multi-turn LLM agent RL.

Principles

• Response-level entropy governs credit assignment.
• Entropy dynamics are shaped by advantage and surprisal.
• Adaptive modulation improves exploration-exploitation.

Method

AEM computes a length-normalized entropy proxy for each response, then applies a self-calibrated, monotone decreasing map to derive a modulation coefficient (alpha) for the base advantage, rescaling it to regulate entropy dynamics.

In practice

• Integrate AEM as a plug-in to existing RL advantage estimators.
• Apply AEM to multi-turn LLM agent tasks for improved performance.
• Utilize response-level uncertainty for finer credit assignment.

Topics

• Adaptive Entropy Modulation
• Multi-Turn Agentic RL
• LLM Agents
• Credit Assignment
• Response-Level Entropy

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

Related on AIssential

• AEM: Adaptive Entropy Modulation for Multi-Turn Agentic Reinforcement Learning — AEM improves LLM agent RL by adaptively modulating response-level entropy for better exploration-exploitation without extra supervision.
• Does RL Expand the Capability Boundary of LLM Agents? A PASS@(k,T) Analysis — RL expands LLM agent capabilities for compositional tool use, unlike its role in static reasoning.
• DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination — Multi-agent LLM instability can be resolved by well-posed, entropy-regularized equilibrium selection using HQRE.
• AMA: Adaptive Memory via Multi-Agent Collaboration — AMA uses multi-agent collaboration and hierarchical memory to provide adaptive, consistent long-term memory for LLM agents.
• EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning — EvoTrainer co-evolves LLM policies and training harnesses, moving beyond static recipe search for agentic RL.
• AI 101: Beyond RL: The New Fine-Tuning Stack for LLMs — Modern LLM fine-tuning is evolving into a modular stack, moving beyond monolithic RL to integrate diverse, efficient methods.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.