Researchers propose a framework called SkillBoost to help large language model agents learn from past interactions and adapt to new situations. The approach involves optimizing skills as if they were model parameters, but with constraints to prevent overfitting. This is achieved through a three-stage process: structured exploitation, prior-guided exploration, and verified acceptance. Experiments show that SkillBoost outperforms other methods in mitigating overfitting and achieving state-of-the-art performance. --- Why it matters: This matters because it addresses the challenge of skill overfitting in large language models, which can limit their ability to generalize to new situations. By providing a framework for constrained exploration-exploitation, SkillBoost has implications for improving the robustness and adaptability of AI systems. Source: https://arxiv.org/abs/2607.26643