Language agents get a second trainable policy: JoMoHa learns the harness too, averaging 70.3% success across three environments
A language agent's performance depends jointly on its model weights and on the harness, the runtime program that organizes how those weights are used, so optimizing either alone is constrained by the other. JoMoHa trains a harness-generating Engineer and a task-executing Solver in one reinforcement-learning process, using the same task outcome to update both policies. It reaches 97.0% success on ALFWorld, 45.8% on WebShop and 68.0% on DBBench, an equally weighted average of 70.3%, above the evaluated weight-only, harness-only and alternating-training recipes.