ARBOR: Online Process Rewards via a Reusable Rubric Buffer for Search Agents
LLM-based search agents are trained predominantly with outcome-only reward, leaving the search process itself unsupervised. This signal degenerates on outcome-homogeneous groups where all sampled trajectories share the same correctness, yielding zero within-group advantage and no gradient. Existing process supervision either trains a costly verifier or generates per-query rubrics that are inconsistent across queries and discarded after one use. We propose ARBOR (Adaptive Rubric Buffer for Online Reward), a reusable process-reward framework that maintains a rubric memory shared across queries. Query-local drafts induced from contrastive trajectories are admitted, consolidated into cross-qu...
| Authors | Zheng Liu, Longxiang Zhang, Xintong Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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