DreamPRM-1.5: Unlocking the Potential of Each Instance for Multimodal Process Reward Model Training

Training multimodal process reward models (PRMs) is hard due to (i) distribution shift between training set and test set and (ii) quality imbalance across training data samples. While domain-level reweighting (e.g., DreamPRM) aligns training with test-time objectives, it leaves a clear gap to an oracle upper bound (pass@N), even under a "sanity check" that uses test set data to probe headroom -- pointing to meta-level under-parameterization. We introduce DreamPRM-1.5, an instance-level reweighting framework that assigns an adaptive weight to every training example via bi-level optimization. To realize instance reweighting across scales, we develop two complementary regimes: Instance Table, which learns explicit per-sample weights and excels on small/medium data, and Instance Net, a lightweight neural network that generalizes better and scales to large corpora. A practical, stable training recipe -- time-scale matching between upper/lower updates, cold-start initialization, and bounded-range weights -- prevents divergence. Integrated with test-time scaling, DreamPRM-1.5 attains 84.6 accuracy on the MMMU validation set, 31.3 accuracy on R-Bench-V and, when paired with a leading backbone (e.g., GPT-5-mini), achieves first-place results on public multimodal reasoning leaderboards. Moreover, extensive experiments, including benchmark evaluations, baseline comparisons, and a sanity check, demonstrate that DreamPRM-1.5 closes the gap toward the oracle, achieves leading performance, and trains stably.