Agent as Coach: Learning to Teach from Student Behavior

Rubrics have become the basis for both evaluation and training feedback on tasks without a single correct answer, yet they describe what a good answer looks like rather than how a student should produce one. Experience extracted from evaluated student behavior can provide this actionable procedure. Expanding the pool of student behavior yields more useful experience, but a larger pool also creates a long-context challenge that lowers the quality of experience summarized from it in one pass. We introduce Agent as Coach, a coach that inspects an external behavior pool, reads selected attempts, and compresses observations before writing experience. Its acquisition and writing actions are jointly trained through the improvement of a frozen student, so the coach learns how to teach from how the student's behavior changes. We then internalize the experience into the student through training, so that the student learns to generate and use experience itself. Across multiple benchmarks with Qwen3-8B-Base and Qwen3.5-9B-Base as students, the complete pipeline consistently outperforms other rubric-based and experience-based methods, improving over the strongest of them by 4.9 and 3.8 points, respectively. Further experiments show that scored student behavior carries information beyond the rubric, that agentic reading avoids the decline of one-pass reading, and that the student learns from the experience itself, indicating that a coach can learn to teach from student behavior.