SkillOpt: Executive Strategy for Self-Evolving Agent Skills
- Published
- Source
- arXiv
- Paper number
- 225
- Field
- AI / General
- arXiv ID
- 2605.23904
Key points
- A skill optimized for a large model, such as GPT-4o, can be successfully applied to a smaller model, such as GPT-4o-mini, and the smaller model often captures much of the improvement, which suggests that the skill provides general procedural knowledge missing from the smaller model's weights.
- A skill learned in one execution environment, such as the default chat interface, often remains valid when transferred to a more complex agent harness, such as a code execution sandbox, which indicates that the learned rule captures task-level logic rather than exploiting quirks of the training environment.
- Procedures learned on one dataset, such as OlympiadBench, also improved performance on a related but distinct dataset, such as Omni-MATH, further showing that SkillOpt does not merely overfit to a specific set of training examples.
Paper links
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