ASH: Agents that Self-Hone via Embodied Learning

Mastering long-horizon embodied tasks remains a fundamental challenge for AI, as current meth- ods often fail due to noisy data or intractable re- ward engineering. We introduce ASH, a fully autonomous agentic system that overcomes these limitations without any human involvement: no reward shaping, no expert annotation, and no domain-specific data curation. When encounter- ing an impasse, ASH uses its own experience to retrieve, and learn from relevant internet video. Evaluated in Pokémon Emerald—a complex RPG spanning dozens of hours—ASH dramatically out- performs baselines: while behavioral cloning and general purpose foundation models (Qwen Team, 2026) collapse to near-zero milestone comple- tion within the first few minutes, ASH sustains robust progression across multi-hour gameplay by continuously and autonomously acquiring new skills. This demonstrates that fully autonomous, self-improving agents are a scalable path for open- ended, long-horizon embodied learning.