JoMoHa co-trains harness writer and task solver with RL
The result reframes harnesses as trainable components rather than fixed scaffolding, with separate optimisation histories as the trick that keeps co-training stable. Agent builders iterating on tool loops or environment code can apply the same decoupled-credit-assignment idea without changing their base models. Teams benchmarking frameworks like ALFWorld or WebShop setups get a new reference point: 70.3% average and 97.0% on ALFWorld, above both weight-only and harness-only baselines. Code and a Chat with Paper companion are linked for hands-on study.
