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AEM: Adaptive Entropy Modulation for Multi-Turn Age... | AI Research

Key Takeaways

  • AEM: Adaptive Entropy Modulation for Multi-Turn Agentic Reinforcement Learning Reinforcement learning (RL) is a powerful tool for training AI agents to perfo...
  • Reinforcement learning (RL) has significantly advanced the ability of large language model (LLM) agents to interact with environments and solve multi-turn tasks.
  • Yet effective training remains challenging, as sparse, outcome-only rewards make it difficult to assign credit to individual steps in an agent's action trajectory.
  • This paper presents AEM, a supervision-free credit assignment method that adaptively modulates entropy dynamics during RL training to achieve a more effective exploration-exploitation trade-off.
  • Specifically, we derive a practical proxy to reshape training dynamics, enabling a natural transition from exploration to exploitation.
Paper AbstractExpand

Reinforcement learning (RL) has significantly advanced the ability of large language model (LLM) agents to interact with environments and solve multi-turn tasks. Yet effective training remains challenging, as sparse, outcome-only rewards make it difficult to assign credit to individual steps in an agent's action trajectory. A common remedy is to introduce dense intermediate supervision, such as process reward models or auxiliary self-supervised signals, but this increases supervision and tuning complexity and often generalizes poorly across tasks and domains. This paper presents AEM, a supervision-free credit assignment method that adaptively modulates entropy dynamics during RL training to achieve a more effective exploration-exploitation trade-off. Theoretically, we elevate entropy analysis from the token level to the response level to reduce token sampling variance and show that entropy drift under natural gradients is intrinsically governed by the product of the advantage and the relative response surprisal. Specifically, we derive a practical proxy to reshape training dynamics, enabling a natural transition from exploration to exploitation. Extensive experiments across various benchmarks and models ranging from 1.5B to 32B parameters demonstrate the effectiveness of AEM, including a notable 1.4 percent gain when integrated into a state-of-the-art baseline on the highly challenging SWE-bench-Verified benchmark.

AEM: Adaptive Entropy Modulation for Multi-Turn Agentic Reinforcement Learning
Reinforcement learning (RL) is a powerful tool for training AI agents to perform complex, multi-turn tasks, but it often struggles with "sparse rewards." Because agents only receive feedback at the very end of a long sequence of actions, it is difficult to determine which specific steps were helpful and which were not. Existing solutions often require extra human-labeled data or complex, computationally expensive setups. AEM (Adaptive Entropy Modulation) offers a new, supervision-free approach that improves how agents learn by automatically adjusting the balance between exploring new strategies and exploiting known successful ones based on the agent's own internal uncertainty.

Understanding Entropy as a Learning Signal

The core innovation of AEM lies in how it interprets "entropy"—a measure of the agent's uncertainty. While traditional methods often look at entropy at the individual token level, AEM elevates this analysis to the "response level." By looking at the entire action taken by the agent, the researchers found that they could create a more stable and reliable signal for credit assignment. They mathematically proved that the way an agent’s uncertainty changes during training is directly linked to the advantage of its actions and how "surprising" those actions are. This allows the model to distinguish between confident, high-quality decisions and exploratory, uncertain ones without needing external guidance.

How AEM Modulates Training

AEM acts as a "plug-in" for existing RL training pipelines. It calculates a modulation coefficient for each response the agent generates. If a response is relatively "surprising" (high uncertainty), AEM adjusts the learning signal to either amplify or dampen the impact of that action on the model's future behavior.
This process is self-calibrating: it automatically identifies which responses are more or less certain within a group of attempts. By scaling the "advantage" (the score assigned to an action) using these coefficients, AEM forces the model to prioritize actions that lead to better outcomes while naturally managing the agent's tendency to explore.

A Natural Transition from Exploration to Exploitation

A major challenge in training AI agents is knowing when to stop experimenting and start focusing on what works. AEM handles this transition automatically. In the early stages of training, when the agent is making many mistakes, AEM applies pressure to increase entropy, which encourages the agent to keep exploring different strategies. As the agent begins to find successful paths and the quality of its responses improves, the modulation shifts to favor exploitation, helping the model converge on a high-performing policy. This happens without the need for hand-crafted schedules or explicit regularization rules.

Performance and Versatility

The researchers tested AEM across a variety of benchmarks, including ALFWorld, WebShop, and the highly challenging SWE-bench-Verified, using models ranging from 1.5 billion to 32 billion parameters. The results demonstrate that AEM consistently improves upon existing baseline methods, achieving a peak gain of 8.8% in some scenarios. Notably, it provided a 1.4% performance boost on the SWE-bench-Verified benchmark when integrated into a state-of-the-art baseline. These findings suggest that using response-level entropy as an internal guide is a highly effective way to optimize LLM agents for complex, multi-turn environments.

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