SkillOpt: Agent skills as trainable parameters

Microsoft Research · 2 weeks ago

SkillOpt treats agent skill files as trainable parameters that can be optimized through a controlled training loop rather than manual editing, using bounded text edits and validation gating to improve performance without modifying model weights. Across six benchmarks, seven models, and three execution modes (52 evaluation cells total), SkillOpt achieved best or tied-best results, with GPT-5.5 improving from 58.8 to 82.3 on average across benchmarks. The optimized skills remain compact (median 920 tokens with one to four accepted edits), transfer across model scales and execution environments, and enable smaller models to match larger baselines without additional inference costs.