Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution
Skills, reusable procedural guidance added at inference, can substantially improve LLM downstream performance (Li et al., 2026). Prior work retrieves skills from a bank by semantic relevance, then uses them as inference-time patches or for model distillation. The individual utility of each skill, however, is largely neglected. We first show that, in on-policy distillation where skill-conditioned policies serve as teachers, fewer than 25% of retrieved skills provide useful distillation signals. We then propose SGUID, a method for selecting a compact subset of skills for distillation. SGUID retains a skill only if it consistently yields effective learning signals during training. The select...
| Authors | Yuhan Liu, Xiyao Ma, Zhongkai Sun et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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