OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Search
Agentic search enables language models to solve knowledge-intensive tasks by adaptively acquiring external evidence over multiple steps. Reinforcement learning with verifiable rewards (RLVR) has emerged as a widely adopted training paradigm for search agents, yet outcome-only rewards are sparse and provide limited credit assignment for intermediate search actions. Existing process-reward methods therefore seek to densify supervision through proxy signals, external evaluators, or likelihood-based information gain. However, proxy rewards can deviate from the final outcome objective, while fixed evaluators can become stale as the search policy evolves, leading to unreliable process supervisi...
| Authors | Erhan Zhang, Yiqun Chen, Zechun Niu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 April 2026 |
| Last updated at source | 23 May 2026 |
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