1 citations · 1 across the 11 of their papers we have counts for
13 papers
CoCA: Criterion-Aware Credit Assignment for Rubric-Based Reinforcement Learning
Guangcheng Zhu, Yingfan Ma, Xing Zheng +7
Rubric-based reinforcement learning (RL) evaluates responses against explicit criteria to construct rewards, offering a viable way to train large language models on open-ended task…
PreSO: Privileged Representation Isolation for On-Policy Self-Distillation
Yuhang Yang, Mingxuan Xia, Chao Ye +6
On-policy self-distillation (OPSD) uses a self-teacher conditioned on privileged information to turn the sparse outcome signal of reinforcement learning with verifiable rewards (RL…
DE-Venus: A Data-Efficient RLVR Framework for Large Language Models
Shenzhi Yang, Guangcheng Zhu, Kai Tang +11
Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning, but its practical scaling is constrained by expensive on-policy rollouts and the cost…
CriPO: Enhancing Rubric-based RL via Self-Distillation
Mingxuan Xia, Yuhang Yang, Chao Ye +8
Rubric-based Reinforcement Learning (RL) has recently shown promise in improving Large Language Models (LLMs) on open-ended tasks. A widely recognized limitation of rubric-based RL…
OPRD: On-Policy Representation Distillation
Shenzhi Yang, Guangcheng Zhu, Bowen Song +8
On-policy distillation (OPD) supervises the student exclusively in the output space by matching next-token distributions. This paradigm suffers from two limitations: (i) a high-var…
GeoMin: Data-Efficient Semi-Supervised RLVR via Geometric Distribution Modeling
Guangcheng Zhu, Shenzhi Yang, Haobo Wang +9
Reinforcement learning with verifiable rewards (RLVR) significantly advances LLM reasoning, yet it faces a dilemma: standard supervised scaling is throttled by high annotation cost…
Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots
Guangcheng Zhu, Shenzhi Yang, Haobo Wang +7
Reinforcement learning with verifiable rewards (RLVR) has greatly advanced large reasoning models (LRMs), but it requires timely training on a huge fully-annotated dataset. To this…
Can LLMs Learn to Reason Robustly under Noisy Supervision?
Shenzhi Yang, Guangcheng Zhu, Bowen Song +7
Reinforcement Learning with Verifiable Rewards (RLVR) effectively trains reasoning models that rely on abundant perfect labels, but its vulnerability to unavoidable noisy labels du…
AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents
Shengda Fan, Xuyan Ye, Yupeng Huo +9
While Large Language Models (LLMs) have evolved into tool-using agents, they remain brittle in long-horizon interactions. Unlike mathematical reasoning where errors are often recti…
TraPO: A Semi-Supervised Reinforcement Learning Framework for Boosting LLM Reasoning
Shenzhi Yang, Guangcheng Zhu, Xing Zheng +7
Reinforcement learning with verifiable rewards (RLVR) has proven effective in training large reasoning models (LRMs) by leveraging answer-verifiable signals to guide policy optimiz…
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning
Zhong Zhang, Yaxi Lu, Yikun Fu +22
The recent progress of large language model agents has opened new possibilities for automating tasks through graphical user interfaces (GUIs), especially in mobile environments whe…
Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance
Yaxi Lu, Shenzhi Yang, Cheng Qian +12
Agents powered by large language models have shown remarkable abilities in solving complex tasks. However, most agent systems remain reactive, limiting their effectiveness in scena…
Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical Perspective
Xinhao Yao, Xiaolin Hu, Shenzhi Yang +1
Pre-trained large language models (LLMs) based on Transformer have demonstrated striking in-context learning (ICL) abilities. With a few demonstration input-label pairs, they can p…