Continual Harness: Online Adaptation for Self-Improving Foundation Agents
Summary
The paper introduces 'Continual Harness,' a framework enabling embodied AI agents to self-improve online without environment resets. It demonstrates significant progress in playing Pokémon games, achieving human-level performance through automated prompt and skill refinement.
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Paper page - Continual Harness: Online Adaptation for Self-Improving Foundation Agents
Source: https://huggingface.co/papers/2605.09998
Abstract
A self-improving AI system for embodied agents autonomously refines its own prompts, skills, and memory through continuous learning without environment resets, achieving human-level performance in complex video games.
Coding harnesses such as Claude Code and OpenHands wrap foundation models with tools, memory, and planning, but no equivalent exists forembodied agents’long-horizon partial-observability decision-making. We first report our Gemini Plays Pokemon (GPP) experiments. With iterative human-in-the-loop harness refinement, GPP became the first AI system to complete Pokemon Blue, Yellow Legacy on hard mode, and Crystal without a lost battle. In the hardest stages, the agent itself began iterating on its strategy through long-context memory, surfacing emergentself-improvement signalsalongside human-in-the-loop refinement.Continual Harnessremoves the human fully from this loop: a reset-free self-improving harness forembodied agentsthat formalizes and automates what we observed. Starting from only a minimal environment interface, the agent alternates between acting and refining its own prompt, sub-agents, skills, and memory, drawing on any past trajectory data.Prompt-optimization methodsrequire episode resets;Continual Harnessadapts online within a single run. On Pokemon Red and Emerald acrossfrontier models,Continual Harnessstarting from scratch substantially reduces button-press cost relative to the minimalist baseline and recovers a majority of the gap to a hand-engineered expert harness, with capability-dependent gains, despite starting from the same raw interface with no curated knowledge, no hand-crafted tools, and no domain scaffolding. We then close the loop with the model itself: anonline process-reward co-learning loop, in which an open-source agent’s rollouts through the refining harness are relabeled by a frontier teacher and used to update the model, drives sustained in-game milestone progress on Pokemon Red without resetting the environment between training iterations.
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