Effective Reinforcement Learning for Agentic Search by Recycling Zero-Variance Queries During Training
The use of GRPO-style algorithms has become the standard strategy for training LLM search agents under outcome-only rewards. With these algorithms, a query contributes to parameter updates only when its rollout group mixes successes and failures; all-correct (too-easy) and all-incorrect (too-hard) groups are zero-variance and waste rollout cost. Existing approaches treat zero-variance as a static property and either discard or pre-filter such groups. We hypothesize and empirically validate that queries flip between zero-variance and signal-bearing states as the policy evolves during training. Building on this intuition, we propose query recycling, which returns zero-variance groups to a m...
| Authors | João Coelho, João Magalhães, Bruno Martins et al. |
|---|---|
| Primary category | cs.IR |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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