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Harness-1: 20B RL Search Agent Offloads State to Environment
May 31, 2026
Harness-1 is a 20B-parameter search agent trained with RL inside a stateful harness that externalizes working memory — candidate pools, evidence links, verification records — rather than forcing the policy to manage bookkeeping. This separation lets RL focus on semantic search decisions, reducing the burden on the model's context and improving reliability of recoverable state.
paper
HOW THIS AFFECTS YOU
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builderYou can adopt the harness pattern to offload state management from your search agent's context window, potentially improving reliability without scaling model size.
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researcherThe state-externalizing harness architecture offers a concrete alternative to transcript-growing policies, worth examining for RL training efficiency and generalization.