SkillCome: Group Contrast Skill Optimization with Dual Memory
Quick summary
arXiv:2609.37128v1 Announce Type: new Abstract: Skill evolution improves the capabilities of large language models by analyzing trajectories generated under a given skill and modifying the skill accordingly. Existing approaches typically generate a single trajectory per question. However, this provides insufficient optimization signals since it requires inferring effective skill edits from a solitary path. It is difficult to pinpoint which actions caused the failure in a failed trajectory, or to determine which actions in a successful one should be incorporated into the skill. Furthermore, the
Key takeaways
- arXiv:2609.37128v1 Announce Type: new Abstract: Skill evolution improves the capabilities of large language models by analyzing trajectories generated under a given skill and modifying the skill accordingly.
- Existing approaches typically generate a single trajectory per question.
- However, this provides insufficient optimization signals since it requires inferring effective skill edits from a solitary path.
Why it matters
The importance of “SkillCome: Group Contrast Skill Optimization with Dual Memory” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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