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Self-HPO jointly optimizes coding-agent policies and harnesses

huggingface.co by Zhepei Wei et al. Editor score 8/10

Self-HPO alternates harness updates proposed from policy failure analysis with RL policy updates under the selected harness; it reports improvements over harness-only and policy-only baselines across three coding benchmarks. Joint optimization addresses the mismatch that can arise when a harness and policy evolve separately.

What: Self-HPO is a recursive framework that jointly optimizes coding-agent harnesses and policy models.
Method: The current policy analyzes failures to propose harnesses; reinforcement learning updates the policy under a selected harness, and hard tasks guide the next search.
Why it matters: Jointly tuning both components improved benchmark results over harness-only and policy-only baselines, with gains generalizing to out-of-distribution settings.

Self-HPO loops from policy failure analysis through harness selection and training back to the policy. Sketched from the article's own text.

Read on huggingface.co → Code · 5 stars

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