HarnessMDP: Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning
The execution harness is a critical component of LLM agents, shaping how the agent interacts with its environment and organizes multi-step execution. Prior work has shown that improving tools, workflows, and other harness components can substantially improve agent performance. This suggests that the harness can be treated as a learnable control layer that adapts execution decisions from experience, rather than as a predetermined execution configuration. In this work, we introduce HarnessMDP, a learning-based framework that makes harness control itself learnable. HarnessMDP formulates agent execution as a finite-horizon Markov decision process, where the harness learns to select structural...
| Authors | Haiwen Yi, Xinyuan Song |
|---|---|
| Primary category | cs.LG |
| Published | 5 July 2026 |
| Last updated at source | 15 October 2026 |
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