Most search agents are trained as policies over a growing transcript. The model decides how to search. It must also remember what it saw, which evidence matters, and which claims it checked. A team of researchers from University of Illinois Urbana-Champaign, UC Berkeley, and Chroma argues this asks too much. Reinforcement learning ends up optimizing both search decisions and routine bookkeeping at once.
Their answer is Harness-1, a 20B retrieval subagent built on gpt-oss-20b. It was trained with reinforcement learning inside a stateful search harness. The harness holds the bookkeeping. The policy keeps the semantic decisions. The weights and harness code are publicly released.
https://arxiv.org/pdf/2606.02373
What is Harness-1 Actually
Harness-1 produces a ranked set of documents for a downstream answering model. It does not answer questions itself. It runs inside a state-machine harness centered on a per-episode WORKINGMEMORY.
