SkillOpt: Agent skills as trainable parameters
Microsoft Research Yifan Yang, Xuemei Gao, Qi Dai, Bei Liu, Kai Qiu, Dongdong Chen, Chong Luo
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.
Why it matters
AI agents often fail because their instructions, or skills, are manually modified with no guarantee of improvement. Learn how SkillOpt turns skill editing into a training process, making agent behavior more reliable without changing model weights. The post SkillOpt: Agent skills as trainable parameters appeared first on Microsoft Research.
