Seungyeon Kim

The work proposes Recursive Harness Distillation (RHD): a strong agent (GPT-6 Astra) first interacts with a frozen vision-language-action model and distills effective intervention strategies into a reusable playbook, then recursively revises that playbook using the light agent's (GPT-5.6 Luna) execution feedback, improving manipulation without updating any model parameters—real-world success rises from 37.3% to 64.0%, the light agent with the playbook reaches 66.7% on SimplerEnv Bridge versus 41.7% for GR00T alone, and the same playbook also lifts the strong agent to 79.2%.

The work proposes Recursive Harness Distillation (RHD): a strong agent (GPT-6 Astra) first interacts with a frozen vision-language-action model and distills effective intervention strategies into a reusable playbook, then recursively revises that playbook using the light agent's (GPT-5.6 Luna) execution feedback, improving manipulation without updating any model parameters—real-world success rises from 37.3% to 64.0%, the light agent with the playbook reaches 66.7% on SimplerEnv Bridge versus 41.7% for GR00T alone, and the same playbook also lifts the strong agent to 79.2%.