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Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution

Paper recorded by Signals 4 on 2026-10-08 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-08 on arXiv · recorded by Signals 4 on 2026-10-09

Category: cs.CL · 自然语言处理 · first seen 2026-10-09

Abstract

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 p

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#285 most recent of 660 cs.CL papers we have recorded · ↑ newer: Latent Core Tokenizer: Compress, but Meaningfully · ↓ older: Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Dist
Cite this page: Which Skill to Distill? SGUID: Selecting a Compact Skill Bank for Model-Skill Co-Evolution: the #285 most recent of 660 cs.CL papers we have recorded (as of 2026-10-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/which-skill-to-distill-sguid-selecting-a-compact-skill-bank-for-model-skill-co-e.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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