Distributional Process Reward Models: Calibrated Prediction of Future Rewards via Conditional Optimal Transport
Inference-time scaling methods rely on Process Reward Models (PRMs), which are often poorly calibrated and overestimate success probabilities. We propose, to our knowledge, the first use of conditional optimal transport for calibrating PRMs, modifying conditional OT (CondOT) map learning \cite{bunne2022supervised} to estimate a monotonic conditional quantile function over success probabilities estimated by the PRM, conditioned on PRM hidden states. This yields structurally valid quantile estimates and enables efficient extraction of confidence bounds at arbitrary levels, which we integrate into the instance-adaptive scaling (IAS) framework of \cite{park2025know}. We evaluate on mathematic...
| Authors | Rachel Ma, Dylan Hadfield-Menell, Kristjan Greenewald |
|---|---|
| Primary category | cs.LG |
| Published | 7 May 2026 |
| Last updated at source | 12 May 2026 |
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