Skill²-Bench is a benchmark of multi-step tasks that force LLMs to switch between skills, introduced in the paper "Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning".
Long-horizon tasks require models to switch between skills, not just execute a single skill well. Each Skill²-Bench task embeds a sequence of 2–10 steps in a coherent real-world scenario, where consecutive steps draw on different skills (e.g., algorithm design… See the full description on the dataset page:
https://huggingface.co/datasets/yinghuihe/Skill2-Bench.
