Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
DreamPRM: Domain-reweighted Process Reward Model for Multimodal Reasoning
Authors: Qi Cao, Ruiyi Wang, Ruiyi Zhang, Sai Ashish Somayajula, Pengtao Xie
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 27,921 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,829 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple multimodal reasoning benchmarks covering both mathematical and general reasoning show that test-time scaling with Dream PRM consistently improves the performance of state-of-the-art MLLMs. |
| Researcher Affiliation | Academia | Qi Cao University of California, San Diego EMAIL Ruiyi Wang University of California, San Diego EMAIL Ruiyi Zhang University of California, San Diego EMAIL Sai Ashish Somayajula University of California, San Diego EMAIL Pengtao Xie University of California, San Diego Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) EMAIL |
| Pseudocode | No | To solve this optimization problem, we propose an efficient gradient-based algorithm, which is detailed in Appendix A. Appendix A describes the algorithm using equations and text, but it is not formatted as a distinct pseudocode block with typical structured programming constructs. |
| Open Source Code | Yes | Project Page: https://github.com/coder-qicao/Dream PRM |
| Open Datasets | Yes | We use 15 multimodal datasets for lower-level optimization (Dtr), covering four domains: science, chart, geometry, and commonsense, as listed in Appendix Table 2. For upper-level optimization (Dmeta), we adopt the MMMU [79] dataset. |
| Dataset Splits | Yes | After processing and sampling, the training datasets in lower-level Dtr have around 15k examples (1k per each of the 15 domains), while the meta dataset in the upper-level Dmeta has around 1k validation examples from the MMMU [79] dataset. |
| Hardware Specification | Yes | Our method is implemented with Betty [7], and the fine-tuning process takes approximately 10 hours on one NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper mentions "Adam W [32] optimizer" and "Betty [7]" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | In the lower-level optimization, we perform 5 inner gradient steps per outer update (unroll steps = 5) using the Adam W [32] optimizer with learning rate set to 5 × 10−7. In the upper-level optimization, we use the Adam W optimizer (lr = 0.01, weight decay = 10−3) and a Step LR scheduler (step size = 5000, γ = 0.5). In total, Dream PRM is fine-tuned for 10000 iterations. |