analiza aiqbench
This paper is clearly about AI: training dynamics of agentic LLMs under reinforcement learning, so it passes the topic gate. It is a research paper rather than a product or policy item, and its core subject is the training method and its failure modes, which fits Machine Learning best. aiqbench take: The paper reports a recurring entropy spike-and-decay pattern in agent RL, where entropy repeatedly erupts and subsides instead of collapsing as it does in single-turn reasoning RL. It also claims that degenerate behaviours such as sentence duplication and hallucination can be locked in during these spikes and accumulate across cycles. Its proposed fix, SEAL, is an auxiliary loss that separates correct and incorrect trajectories in representation space; the reported gains are benchmark-based and should be checked against independent replication before being treated as settled.
Cyclical Entropy Eruption: Entropy Dynamics in Agent Reinforcement Learning
arXiv:2605.27954v2 Announce Type: replace 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.
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.
Machine Learning (cs.LG)
arXiv:2605.27954 [cs.LG]
(or arXiv:2605.27954v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.27954 Focus to learn more arXiv-issued DOI via DataCite
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