SPIN Unprocessed July 8, 2026 ai_technology research
Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning
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arXiv:2607.05458v1 Announce Type: new Abstract: Large language model (LLM) agents are usually improved by changing prompts, models, or hand-written workflows, while the execution harness around the model is treated as fixed infrastructure. We argue that this harness is itself a learnable control layer. We formalize harness operation as a finite-horizon Harness MDP, where a lightweight controller selects structural execution actions while the LLM executor remains frozen. The controller is trained
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