8 citations · 17 across the 25 of their papers we have counts for
8 papers · 1 filter
Models Under SCOPE: Scalable and Controllable Routing via Pre-hoc Reasoning
Qi Cao, Shuhao Zhang, Ruizhe Zhou +3
Model routing chooses which language model to use for each query. By sending easy queries to cheaper models and hard queries to stronger ones, it can significantly reduce inference…
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: 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…
TapWeight: Reweighting Pretraining Objectives for Task-Adaptive Pretraining
Ruiyi Zhang, Sai Ashish Somayajula, Pengtao Xie
Large-scale general domain pretraining followed by downstream-specific finetuning has become a predominant paradigm in machine learning. However, discrepancies between the pretrain…
BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation
Peijia Qin, Ruiyi Zhang, Pengtao Xie
Parameter-efficient fine-tuning (PEFT) is a flexible and efficient method for adapting large language models (LLMs) to downstream tasks. Among these methods, weight-decomposed low-…
BiLoRA: A Bi-level Optimization Framework for Overfitting-Resilient Low-Rank Adaptation of Large Pre-trained Models
Rushi Qiang, Ruiyi Zhang, Pengtao Xie
Low-rank adaptation (LoRA) is a popular method for fine-tuning large-scale pre-trained models in downstream tasks by learning low-rank incremental matrices. Though LoRA and its var…