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

Qi Cao; Pengtao Xie

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arXiv · 2509.05542

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

Qi Cao·Pengtao Xie

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Abstract

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.

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BibTeX RIS

Qi Cao, Pengtao Xie. 2025-10-21. DreamPRM-1.5: Unlocking the Potential of Each Instance for Multimodal Process Reward Model Training. https://arxiv.org/abs/2509.05542

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