Researchers propose skill entropy as a metric to evaluate and improve long-horizon reasoning in LLMs, addressing limitations of existing benchmarks that focus on isolated skills. The method quantifies a model's ability to switch between distinct skills (e.g., math derivation to scheduling) within multi-step reasoning chains. This approach enables benchmarking and training for cross-skill tasks requiring interdependent, sequential reasoning steps. Read original