SearchMaster: Grounded and Regulated Self-Play for Search Agents
Training LLM-based search agents requires high-quality search data: tasks that demand genuine multi-hop retrieval and trajectories that use search tools effectively. Existing pipelines often depend on human-written tasks, expert demonstrations, or stronger teacher models. We present SearchMaster, a self-play framework that trains a shared LLM policy from search tasks it generates, solves, and verifies in a local search environment. The key challenge is that self-generated tasks and rollouts can yield misleading signals: pseudo multi-hop questions, success-rate difficulty estimates that ignore search depth, and rollouts with excessive opening but little targeted evidence acquisition. Searc...
| Authors | Wentao Tan, Qiong Cao, Jiaqi Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 August 2026 |
| Last updated at source | 12 October 2026 |
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