Beyond Math and Code: Lightweight Corpus-Grounded Process Rewards for Factual Question Answering
Process supervision during reinforcement learning (RL) post-training matters in factual question answering (QA) because responses receiving positive response-level rewards can still contain sentence-level factual errors. Unlike math and code, factual QA lacks inexpensive programmatic checks, making reward computation a bottleneck. Existing methods rely on neural verifiers to score individual sentences, requiring extensive model inference as RL repeats these checks across many sampled responses at every update. We therefore introduce CorVer (Corpus Verify), a lightweight training-time approach to process supervision that derives sentence-level rewards from corpus co-occurrence statistics. ...
| Authors | Shicheng Fan, Haochang Hao, Dehai Min et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 May 2026 |
| Last updated at source | 26 September 2026 |
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