OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Search 事件

OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Search arXiv:2604.03675v3 Announce Type: replace-cross Abstract: 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. Ex