RAWR: Reward Assignment Without Rollouts in Verifiable Domains
Understanding and evaluating multi-step reasoning in LLMs at the level of individual steps remains a key challenge. Process reward models (PRMs) provide a solution by scoring each step, enabling fine-grained supervision and improved reliability. However, training them requires costly human annotation or computationally intensive rollout-based labeling. To solve this, we introduce MCNIG, a scalable method for automatically labeling the quality of individual reasoning steps in any verifiable domain. Its step score, net information gain (NetIG), improves upon single-reference information gain (IG) by comparing the most-supported correct answer against the most-supported incorrect one, yieldi...
| Authors | Corentin Royer, Anna Hedström, Debarun Bhattacharjya et al. |
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| Primary category | cs.CL |
| Published | 18 March 2026 |
| Last updated at source | 29 September 2026 |
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