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Self-HPO framework alternates harness and policy optimization for coding agents
Self-HPO lets a coding agent propose its own harness candidates and then update its policy with reinforcement learning, raising Qwen3.5-35B-A3B's average resolve rate from 57.8% to 64.8% across three coding benchmarks. The paper also reports gains with a stronger external model, such as Opus 4.8, a…
Hugging Face paper introduces CoworkEnv for synthesizing training environments for AI coworker agents
The framework generates executable training environments for AI coworkers and trains PocketWorker models. 9-point gain on multi-turn tasks.
Hugging Face paper introduces HarnessPro for cheaper, stronger AI agent harnesses
HarnessPro evolves agent harnesses without ground-truth tests and outputs lite, medium and heavy variants. 1% versus RHO.