FormalRewardBench: A Benchmark for Formal Theorem Proving Reward Models
Recent neural theorem provers use reinforcement learning with verifiable rewards (RLVR), where proof assistants provide binary correctness signals. While verifiable rewards are cheap and scalable without reward hacking issues, they suffer from sparse credit assignment: models receive no learning signal from difficult problems where partial progress goes unrewarded. This motivates learned reward models that can evaluate proof quality beyond binary verification. However, comparing reward models is challenging since it typically requires expensive RL training ablations. To address this, we introduce \textbf{FormalRewardBench}, the first benchmark for evaluating reward models in formal theore...
| Authors | Zeynel A. Uluşan, Burak S. Akbudak, Can S. Erer et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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