Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning

TL;DR - An arXiv paper introduces "Skill Entropy," a measure of how hard it is to switch reasoning skills mid-chain, and uses it both to benchmark and to train LLMs on cross-skill long-horizon tasks. It matters because it exposes and helps close a distinct failure mode — skill switching — that single-skill benchmarks miss.

• Defines cross-skill long-horizon tasks (multi-step problems where consecutive steps need different reasoning skills and depend on prior outputs) and quantifies transition difficulty via Skill Entropy.
• Releases Skill²-Bench: 558 skills across 9 verifiable and open-ended domains, with per-task skill-entropy scores bucketed into three difficulty levels.
• Evaluating 8 frontier and 4 open-source models reveals a "skill-switching gap": accuracy degrades as task skill entropy rises.
• Skill-Entropy RL has the model predict both the answer and the skill per step, rewarding step correctness plus alignment with the gold skill sequence; Skill²-Bench scores rise 34.4%→68.4% (Qwen3-4B-Instruct) and 14.6%→40.1% (Qwen3-1.7B), and the pipeline transfers to existing data like OpenR1-Math. Code at github.com/Gen-Verse/Skill-Entropy-RL.