42 citations · 53 across the 16 of their papers we have counts for
20 papers
SecureVibe: Making Vibe Coding More Secure
Danqing Wang, Baolin Peng, Zhepei Wei +8
As vibe coding becomes increasingly capable and widespread, security vulnerabilities in even functionally correct solutions are a growing concern. When investigating functionally c…
Negative Self-Distillation: Learning to Reason by Avoiding Flaws
Rongcan Pei, Zhepei Wei, Shuyao Xu +3
On-Policy Self-Distillation (OPSD) has emerged as a popular paradigm for large language model (LLM) self-improvement, allowing models to act as their own teachers by leveraging pri…
You Only Need Minimal RLVR Training: Extrapolating LLMs via Rank-1 Trajectories
Zhepei Wei, Xinyu Zhu, Wei-Lin Chen +3
Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving reasoning in large language models (LLMs), yet the underlying geometry of the res…
G-Zero: Self-Play for Open-Ended Generation from Zero Data
Chengsong Huang, Haolin Liu, Tong Zheng +7
Self-evolving LLMs excel in verifiable domains but struggle in open-ended tasks, where reliance on proxy LLM judges introduces capability bottlenecks and reward hacking. To overcom…
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation
Song Wang, Zihan Chen, Peng Wang +5
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources to address their limitations in accessing up-to-date or special…
Aligning Large Language Models via Fully Self-Synthetic Data
Shangjian Yin, Zhepei Wei, Xinyu Zhu +2
Traditional reinforcement learning from human feedback (RLHF) for large language models (LLMs) relies on expensive human-annotated datasets, while Reinforcement Learning from AI Fe…
Beyond Outcome Reward: Decoupling Search and Answering Improves LLM Agents
Yiding Wang, Zhepei Wei, Xinyu Zhu +1
Enabling large language models (LLMs) to utilize search tools offers a promising path to overcoming fundamental limitations such as knowledge cutoffs and hallucinations. Recent wor…
TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning
Zhepei Wei, Xiao Yang, Kai Sun +12
While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination and untruthful responses, particularly…
AdaDecode: Accelerating LLM Decoding with Adaptive Layer Parallelism
Zhepei Wei, Wei-Lin Chen, Xinyu Zhu +1
Large language models (LLMs) are increasingly used for long-content generation (e.g., long Chain-of-Thought reasoning) where decoding efficiency becomes a critical bottleneck: Auto…
PersonaAgent: Bridging Memory and Action for Personalized LLM Agents
Weizhi Zhang, Xinyang Zhang, Chenwei Zhang +12
Large Language Model (LLM) empowered agents have recently emerged as advanced paradigms that exhibit impressive capabilities in a wide range of domains and tasks. Despite their pot…
The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning
Xinyu Zhu, Mengzhou Xia, Zhepei Wei +3
Reinforcement learning with verifiable rewards (RLVR) is a promising approach for training language models (LMs) on reasoning tasks that elicit emergent long chains of thought (CoT…
WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning
Zhepei Wei, Wenlin Yao, Yao Liu +9
While reinforcement learning (RL) has demonstrated remarkable success in enhancing large language models (LLMs), it has primarily focused on single-turn tasks such as solving math…
Do LLM Evaluators Prefer Themselves for a Reason?
Wei-Lin Chen, Zhepei Wei, Xinyu Zhu +2
Large language models (LLMs) are increasingly used as automatic evaluators in applications such as benchmarking, reward modeling, and self-refinement. Prior work highlights a poten…
InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales
Zhepei Wei, Wei-Lin Chen, Yu Meng
Retrieval-augmented generation (RAG) has shown promising potential to enhance the accuracy and factuality of language models (LMs). However, imperfect retrievers or noisy corpora c…
Incentivized Truthful Communication for Federated Bandits
Zhepei Wei, Chuanhao Li, Tianze Ren +2
To enhance the efficiency and practicality of federated bandit learning, recent advances have introduced incentives to motivate communication among clients, where a client particip…
Incentivized Communication for Federated Bandits
Zhepei Wei, Chuanhao Li, Haifeng Xu +1
Most existing works on federated bandits take it for granted that all clients are altruistic about sharing their data with the server for the collective good whenever needed. Despi…
Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables
Erxin Yu, Lan Du, Yuan Jin +2
Recently, discrete latent variable models have received a surge of interest in both Natural Language Processing (NLP) and Computer Vision (CV), attributed to their comparable perfo…
Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning
Zhining Liu, Pengfei Wei, Zhepei Wei +5
Class-imbalance is a common problem in machine learning practice. Typical Imbalanced Learning (IL) methods balance the data via intuitive class-wise resampling or reweighting. Howe…
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
Zhepei Wei, Jianlin Su, Yue Wang +2
Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction. However, few existing works excel in solving the overlapping triple pr…
Jointly Modeling Hierarchical and Horizontal Features for Relational Triple Extraction
Zhepei Wei, Yantao Jia, Yuan Tian +4
Recent works on relational triple extraction have shown the superiority of jointly extracting entities and relations over the pipelined extraction manner. However, most existing jo…