Cyclical Entropy Eruption: Entropy Dynamics in Agent Reinforcement Learning

2026-05-27

AI Summary

This work addresses the periodic entropy explosion phenomenon in reinforcement learning (RL) agent training, which often leads to instability and the persistent accumulation of degenerate behaviors such as hallucination and repetition. The study systematically uncovers, for the first time, a three-phase dynamic mechanism underlying this phenomenon—explosion, decay, and recovery—and introduces SEAL, a lightweight and algorithm-agnostic method that explicitly disentangles correct and erroneous trajectories in the representation space. By incorporating an auxiliary loss to suppress degenerate behaviors at their source, SEAL enhances training stability without reliance on specific model architectures or RL algorithms. Extensive experiments demonstrate that SEAL consistently stabilizes training and significantly improves downstream task performance across diverse benchmarks, model designs, and RL frameworks.

Abstract

Agentic large language models are increasingly used to solve real-world tasks by reasoning over goals, invoking tools, and interacting with external environments. Reinforcement learning provides a natural framework for improving these behaviors, and recent agent RL methods have achieved strong results across domains. However, the training dynamics of agent RL remain poorly understood, limiting our ability to diagnose instabilities and design more effective training algorithms. In this work, we identify a previously underexplored phenomenon in agent RL, which we term cyclical entropy eruption. Unlike single-turn reasoning RL, where entropy typically collapses and stays low, agent RL training exhibits unique recurring cycles of sharp entropy eruption and gradual subsidence. We decompose this dynamic into three phases and provide theoretical and empirical analyses of each, explaining the mechanisms underlying its cyclical oscillation. We further show that degenerate patterns such as sentence duplication and hallucination, once acquired during eruption, can persist and accumulate across cycles. Motivated by these findings, we propose SEAL (Separation-Enhanced Agent Learning), a lightweight auxiliary loss that separates correct and incorrect trajectories in representation space, directly targeting the root cause of entropy eruption. Experiments across multiple benchmarks, models, and RL algorithms demonstrate that SEAL stabilizes training and yields stronger downstream agent performance.

Problem

• cyclical entropy eruption
• agent reinforcement learning
• entropy dynamics
• training instability
• degenerate behaviors

Innovation

• cyclical entropy eruption
• agent reinforcement learning
• entropy dynamics
• representation separation
• SEAL