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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 presents EgoXperience for world-grounded egocentric reconstruction
The paper introduces EgoX-Data-6M, a 50-hour dataset, and EgoX, a model that jointly reconstructs camera, scene, hands and objects from monocular video in one forward pass. 6% lower hand error against the strongest evaluated baselines.
Hugging Face paper proposes collective experience learning for fleets of robots
FERT and FATS let deployed robots share execution experience to improve a common policy without extra expert corrections. 3%.