39 citations · 41 across the 11 of their papers we have counts for
12 papers
GVPO++: Group Variance Policy Optimization for LLM Post-Training and On-Policy Distillation
Kaichen Zhang, Yuzhong Hong, Junwei Bao +4
Post-training plays a pivotal role in enhancing the reasoning capabilities and task-specific expertise of large language models (LLMs). Despite recent advances in post-training met…
PGPO: Potential-Guided Policy Optimization for Multi-Turn Agentic Tasks
Yuyao Zheng, Haipeng Sun, Junwei Bao +4
Group-based reinforcement learning (RL) has become an effective paradigm for LLM post-training, but in multi-turn agentic tasks with sparse terminal rewards, it often provides coar…
FABSVer: Faster Training and Better Self-Verification for LLM Mathematical Reasoning
Haihui Pan, Junwei Bao, Hongfei Jiang +1
While large language models have made significant progress in mathematical reasoning, they remain unreliable at judging the correctness of their own solutions. Existing approaches…
Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment
Jing Zhao, Ting Zhen, Junwei Bao +2
Current alignment methods for Large Language Models (LLMs) rely on compressing vast amounts of human preference data into static, absolute reward functions, leading to data scarcit…
Quality-constrained Entropy Maximization Policy Optimization for LLM Diversity
Haihui Pan, Yuzhong Hong, Kaichen Zhang +4
In many large language model (LLM) alignment applications, users expect not only high-quality outputs but also substantial diversity. However, existing methods often face a fundame…
RSPO: Risk-Seeking Policy Optimization for Pass@k and Max@k Metrics in Large Language Models
Kaichen Zhang, Shenghao Gao, Yuzhong Hong +6
Current large language model post-training optimizes a risk-neutral objective that maximizes expected reward, yet evaluation relies heavily on risk-seeking metrics like Pass@k (at…
GVPO: Group Variance Policy Optimization for Large Language Model Post-Training
Kaichen Zhang, Yuzhong Hong, Junwei Bao +4
Post-training plays a crucial role in refining and aligning large language models to meet specific tasks and human preferences. While recent advancements in post-training technique…
Energy-Based Preference Model Offers Better Offline Alignment than the Bradley-Terry Preference Model
Yuzhong Hong, Hanshan Zhang, Junwei Bao +2
Since the debut of DPO, it has been shown that aligning a target LLM with human preferences via the KL-constrained RLHF loss is mathematically equivalent to a special kind of rewar…
Preference-Oriented Supervised Fine-Tuning: Favoring Target Model Over Aligned Large Language Models
Yuchen Fan, Yuzhong Hong, Qiushi Wang +3
Alignment, endowing a pre-trained Large language model (LLM) with the ability to follow instructions, is crucial for its real-world applications. Conventional supervised fine-tunin…
BoRA: Bi-dimensional Weight-Decomposed Low-Rank Adaptation
Qiushi Wang, Yuchen Fan, Junwei Bao +2
In recent years, Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) have significantly enhanced the adaptability of large-scale pre-trained models. Weig…
Multi-Turn Interactions for Text-to-SQL with Large Language Models
Guanming Xiong, Junwei Bao, Hongfei Jiang +2
This study explores text-to-SQL parsing by leveraging the powerful reasoning capabilities of large language models (LLMs). Despite recent advancements, existing LLM-based methods a…
Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models
Guanming Xiong, Junwei Bao, Wen Zhao
This study explores the realm of knowledge base question answering (KBQA). KBQA is considered a challenging task, particularly in parsing intricate questions into executable logica…