Harness-G: A Graph-Structured Harness for Search Agents
Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation and optimize multi-turn interactions using final-answer rewards. Current studies mainly improve training with denser or more structured credit signals, but rarely examine whether retrieval is properly formulated at the policy-environment interface. We observe pronounced retrieval aliasing during Search-R1 training: rollouts for the same question continue to generate distinct query strings, yet their accumulated evidence sets increasingly overlap. We call this phenomenon retrieval-equivalence collapse; in this regime, trajectories approach utility equivalence with respect to re...
| Authors | Yanning Hou, Haoyuan Chen, Sihang Zhou et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 July 2026 |
| Last updated at source | 12 August 2026 |
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