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原文摘要

Small open models suit private customer-service deployments but trail frontier models in two ways: they miss what the written policy requires in a given situation, and they act inconsistently on what they know. We study whether such a model can close both gaps from its own deployment logs and their reference outcomes, without a stronger teacher model. Our starting point is a Gemma 4 12B agent that reads its domain policy from on-demand skills rather than from its system prompt. In each round, the deployed system serves a fresh batch of tasks; the base model diagnoses failed conversations and writes rules into the skills, and a rule survives only if it passes code checks, counterexample self-review, a paired retest, and a noise-calibrated validation gate. Reinforcement learning (GRPO) then trains a LoRA adapter on complete multi-turn, tool-using conversations. On the retail and airline domains of $τ^2$-bench, without training or selecting on official tasks, the agent after two rounds improves on this starting point by 12.5 pass$^1$ points in retail and 19.5 in airline, and by 18.4 and 22.0 pass$^4$ points. It also exceeds the benchmark's reference agent, which runs the same model with the whole policy in its system prompt, by 11.8 and 8.0 pass$^1$ points and by 22.8 and 16.0 pass$^4$ points, closing most of the gap to three frontier models under the same protocol. A $2\times2$ ablation shows that the rules rescue tasks the agent never solved and carry policy knowledge that RL with the rules in context does not internalize, whereas the trained weights make solved tasks reliable.