HarnessMDP: Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning

HarnessMDP is a learning-based framework that treats the execution harness of large language model agents as a learnable control layer. By formulating agent execution as a finite-horizon Markov decision process, the harness learns to select structural execution actions from the evolving agent state, using offline reinforcement learning from collected trajectories. Experiments on coding tasks, tau-bench, AgentBench, and other evaluation settings show that HarnessMDP consistently improves execution behavior and final task performance over baseline harnesses.

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• llms
• agents
• reinforcement-learning