Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting

Enabling large language model (LLM) agents to accumulate and reuse experience from past interactions remains a central challenge in real-world applications. A promising solution is to treat skills as trainable states and optimize them in the same way as model parameters in neural network training. However, data-driven skill optimization is prone to overfitting to the limited trajectories collected from real environments. Overexploiting these trajectories overfits the current batch, while unconst

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ethics.ai (29 July 2026), “Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting,” evidence record 14582, https://ethics.ai/record/14582 (originally published by arXiv).

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