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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Hongqiang Lin, Chao Liu, Xiaofan Bai, Xuan Jin, Yuhong Li, Nenggan Zheng, Xipeng Cao
• Venue: arXiv: cs.AI
• Published: 2026-07-29
• Collected: 2026-07-30
• URL: http://arxiv.org/abs/2607.26643v1
• PDF: https://arxiv.org/pdf/2607.26643v1
• DOI: UNKNOWN

Plain-Language Brief

The researchers propose SkillBoost, a framework to help large language models accumulate and reuse experience from past interactions more effectively in real-world applications. The framework mitigates overfitting risks by using structured exploitation, prior-guided exploration, and verified acceptance of improved skills.

What the Document Says

• The challenge is to enable large language model (LLM) agents to accumulate and reuse experience from past interactions in real-world applications.
• A solution is to treat skills as trainable states and optimize them like model parameters in neural network training.
• Data-driven skill optimization often overfits to the limited trajectories collected from real environments.
• Overexploiting these trajectories overfits the current batch, while unconstrained exploration causes regression on previously solved cases.
• The paper proposes SkillBoost, a three-stage framework that mitigates both risks: structured exploitation, prior-guided exploration, and verified acceptance.
• Structured exploitation localizes observed failures to editable skill components.
• Prior-guided exploration draws on prior knowledge in the LLM to generate diverse repair candidates.
• Verified acceptance commits a candidate only when it improves performance within a regression bound.
• Experiments across 23 model--benchmark configurations show that SkillBoost achieves state-of-the-art performance while mitigating overfitting.
• SkillBoost outperforms both human-crafted and LLM-generated skills.
• Transfer experiments further show that optimized skills can be reused by other agents on similar tasks.

Key Concepts Mentioned

• large language model (LLM)
• skills
• skill optimization
• overfitting
• exploration--exploitation trade-off
• SkillBoost
• structured exploitation
• prior-guided exploration
• verified acceptance

Claims Made by the Document

• Data-driven skill optimization often overfits to the limited trajectories collected from real environments.
• Overexploiting these trajectories overfits the current batch, while unconstrained exploration causes regression on previously solved cases.
• SkillBoost mitigates both risks and achieves state-of-the-art performance while mitigating overfitting.
• SkillBoost outperforms both human-crafted and LLM-generated skills.

Evidence Used

• Experiments across 23 model--benchmark configurations

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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 unconstrained exploration causes regression on previously solved cases. This tension motivates a constrained search view of skill self-evolution, governed by an exploration--exploitation trade-off. We propose SkillBoost, a three-stage framework that mitigates both risks: structured exploitation localizes observed failures to editable skill components, prior-guided exploration draws on prior knowledge in the LLM to generate diverse repair candidates, and verified acceptance commits a candidate only when it improves performance within a regression bound. Experiments across 23 model--benchmark configurations show that SkillBoost achieves state-of-the-art performance while mitigating overfitting, outperforming both human-crafted and LLM-generated skills. Transfer experiments further show that optimized skills can be reused by other agents on similar tasks.