Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning
Score
78首次提出技能熵并构建跨技能长程推理基准，填补现有评测忽略技能切换的空白。
方法设计严谨，基准覆盖广，实验对比充分，且开源代码，内容完整。
Skill-Entropy RL 可直接用于现成训练数据，显著提升小模型推理能力，具备较强落地价值。
长程推理是当前大模型研究热点，论文预印本时间较新，具有时效优势。
来源为 arXiv 预印本，且提供开源代码，但尚未经过严格同行评审。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper forms a complete pipeline from problem definition and benchmark design to training, with thorough experiments and open-source code, offering clear reference value to the community.
AI commentary
「What impresses me most is that the paper doesn’t stop at observing model failures; it turns the cognitive burden of skill switching into a computable entropy and directly uses it as an RL reward. For researchers in training algorithms, this is a clean signal design. Benchmark designers will also find its difficulty-grading methodology worth considering.」
Quick overview
Long-horizon reasoning requires LLMs to switch between distinct skills within a chain, yet existing benchmarks rarely assess this. This paper introduces Skill Entropy, a measure of switching difficulty, and Skill^2-Bench, a benchmark spanning 558 skills across 9 verifiable domains. Evaluations of 8 frontier and 4 open-source models reveal a clear skill-switching gap: accuracy drops on higher-entropy tasks. To address this, the authors propose Skill-Entropy RL, where the model predicts both answers and the skill used per step, with rewards combining step-level correctness and skill-entropy alignment. On Qwen3-4B-Instruct and Qwen3-1.7B, scores jump from 34.4% to 68.4% and 14.6% to 40.1% respectively, outperforming baselines. The approach also transfers to off-the-shelf datasets like OpenR1-Math, suggesting skill entropy is a reusable training signal. Code is open-sourced.
- Introduces Skill Entropy to quantify cross-skill switching difficulty and builds Skill²-Bench covering 558 skills across 9 domains.
- Evaluates 8 frontier and 4 open-source models, revealing a skill-switching gap: accuracy drops on higher-entropy tasks.
- Proposes Skill-Entropy RL, where the model predicts both answer and skill at each step, with rewards combining correctness and skill-entropy alignment.
- Improves Skill²-Bench scores from 34.4% to 68.4% on Qwen3-4B and from 14.6% to 40.1% on Qwen3-1.7B, transferring to OpenR1-Math as well.
Full text
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