Researchers have introduced Self-HPO, a novel framework designed to recursively improve coding agents by alternating between optimizing the agent's policy and its interaction harness. This approach allows the agent to identify and target its own evolving failure modes through reinforcement learning. Experiments on three coding benchmarks demonstrated that Self-HPO significantly boosts performance, increasing the resolve rate of Qwen3.5-35B-A3B from 57.8% to 64.8% and showing gains that generalize to out-of-distribution settings. The framework's effectiveness is further highlighted by its ability to achieve compounding gains when both components are optimized, and it can even benefit from using a stronger external model as a harness proposer. AI
IMPACT This framework could lead to more capable and reliable coding agents by enabling self-improvement, potentially accelerating software development.
RANK_REASON The cluster describes a new research framework and its performance on coding benchmarks, detailed in a paper and released code/models. [lever_c_demoted from research: ic=1 ai=1.0]
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