Princeton Introduces Skill Entropy to Measure and Boost LLM Cross-Skill Reasoning
princetonu · hf · 2026-08-06
Princeton University introduces Skill Entropy, a metric to measure the difficulty of switching between distinct skills in long-horizon reasoning chains, alongside the Skill²-Bench benchmark covering 558 skills across 9 domains.
Evaluations reveal a 'skill-switching gap' across frontier and open-source models, with accuracy dropping on high-entropy tasks. To address this, the researchers propose Skill-Entropy RL, a framework where the model predicts both the answer and the skill used at each step, rewarding alignment with the gold skill sequence. Experiments show this pipeline lifts benchmark scores to 68.4% on Qwen3-4B and 40.1% on Qwen3-1.7B, and can be easily applied to existing datasets like OpenR1-Math.
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