5 papers
FunPRM: Function-as-Step Process Reward Model with Meta Reward Correction for Code Generation
Ruiyi Zhang, Peijia Qin, Qi Cao +2
Code generation is a core application of large language models (LLMs), yet LLMs still frequently fail on complex programming tasks. Given its success in mathematical reasoning, tes…
DAJ: Data-Reweighted LLM Judge for Test-Time Scaling in Code Generation
Peijia Qin, Ruiyi Zhang, Qi Cao +1
Test-time scaling for code generation commonly relies on Best-of-N selection, in which multiple candidate solutions are sampled from a base model, and the best one is selected by a…
DreamPRM-Code: Function-as-Step Process Reward Model with Label Correction for LLM Coding
Ruiyi Zhang, Peijia Qin, Qi Cao +1
Process Reward Models (PRMs) have become essential for improving Large Language Models (LLMs) via test-time scaling, yet their effectiveness in coding remains limited due to the la…
DreamPRM-1.5: Unlocking the Potential of Each Instance for Multimodal Process Reward Model Training
Qi Cao, Pengtao Xie
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. Wh…
DreamPRM: Domain-Reweighted Process Reward Model for Multimodal Reasoning
Qi Cao, Ruiyi Wang, Ruiyi Zhang +2
Reasoning has substantially improved the performance of large language models (LLMs) on complicated tasks. Central to the current reasoning studies, Process Reward Models (PRMs) of…