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The executable harness surrounding a GUI model determines how observations are assembled, actions are executed, and verification, recovery, and termination are controlled. Compared with harness optimization for non-GUI agents, automatically optimizing this harness poses three coupled challenges: reconciling model inten...
Maximum Likelihood Reinforcement Learning (MaxRL) targets prompt-wise log-success and has shown strong performance on reasoning tasks. Under finite rollout budgets, however, the estimator used by MaxRL attenuates each prompt's likelihood gradient by a factor that depends on its success probability and rollout count. Un...
The EXPonential-weight algorithm for prompt Optimization} (EXPO) is proposed to automatically optimize the task description and meta-instruction in the meta-prompt for LLM-based agents and is extended to additionally optimize the exemplars (i.e., history of interactions) in the meta-prompt to further enhance the perfor...
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