SkillOpt treats agent skills as trainable, versioned parameters
Microsoft Research introduced a method that optimizes reusable agent-skill instructions against task feedback, treating the skill library as an explicit component to train and evaluate.
Why it matters: Skills can improve without changing the base model, but only if their versions, training tasks and held-out effects remain inspectable.