# EvoGuard

Language agents can store experience as natural-language skill documents, but rewriting a skill from a single trajectory after every deployed task lets its quality drift. EvoGuard turns each update into a two-way choice: offline, skills are scored on a private test set, rejection sampling keeps pairs with a clear utility gap, and a selector trained on the logits of two label tokens decides which skill to keep; at deployment, one forward pass settles which of the old and new skills stays. On ALFWorld, with Qwen3-4B and Qwen3-8B executors, it averages 56.5% success, against 45.5% for accepting every rewrite and 36.7% with no skill.

Language agents can store experience as natural-language skill documents, but rewriting a skill from a single trajectory after every deployed task lets its quality drift. EvoGuard turns each update into a two-way choice: offline, skills are scored on a private test set, rejection sampling keeps pairs with a clear utility gap, and a selector trained on the logits of two label tokens decides which skill to keep; at deployment, one forward pass settles which of the old and new skills stays. On ALFWorld, with Qwen3-4B and Qwen3-8B executors, it averages 56.5% success, against 45.5% for accepting every rewrite and 36.7% with no skill.