Harness learning: a small model trained with RL learns to rewrite an agent’s harness from execution feedback

An agent is a model plus a harness: the code that decides when the model is called, which tools it uses and how information flows between steps. Harnesses are normally written by engineers. This preprint (posted 28 Sept 2026) trains a second model, the proposer, to write the edits instead. The proposer sees the task description, the current harness and a report of what happened when it ran, and outputs a code change. The revised harness is run, and its task score is the reward for reinforcement learning. Only the proposer is trained; the solver model stays frozen.

At test time both models are frozen. Adaptation to a new task happens entirely by rewriting the harness over successive rounds, without any weight update. The authors compare this to learning-to-learn methods where the “update” is a program edit instead of a gradient step.

It is a concrete, small-scale form of an agent improving how it works from its own experience, which is the same idea as the larger self-improvement results of 2026, but with open 4B–8B models and standard benchmarks, so others can check it. The reported gains are on Reasoning Gym puzzles and multi-hop question answering; nothing here shows the method working on long real-world tasks or with frontier models. The authors’ affiliations (Carnegie Mellon and Johns Hopkins) are from their known positions and the acknowledgements; the PDF’s first page does not list them.