Search-G1: Co-Evolving Representation-Based Rewards for Selective Grounded Search
Search-augmented language agents should retrieve external information only when necessary and ground their answers in retrieved evidence. Yet terminal correctness assigns equal credit to direct answers from parametric knowledge, evidence-grounded answers, and answers recalled after redundant search. Process supervision and LLM judges provide richer feedback but can require high-cost annotation or inference; low-cost, non-contrastive policy confidence signals do not directly identify whether retrieved evidence changes an answer. We propose Search-G1, a reward framework that couples retrieval necessity with evidence reliance through two intervention-calibrated hidden-state readouts for sele...
| Authors | Ruoxi Cheng, Haoxuan Ma, Hongyi Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 July 2026 |
| Last updated at source | 10 October 2026 |
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