4 citations · 12 across the 9 of their papers we have counts for
9 papers
Cross-Domain Hybrid OPD for Generalizable Search Agents
Hongzhan Chen, Xiaoyu Liu, Dengming Zhang +11
Recent advances in Reinforcement Learning (RL) have substantially improved the capabilities of autonomous search agents, enabling sophisticated planning, and iterative retrieval ov…
Unleashing Implicit Rewards: Prefix-Value Learning for Distribution-Level Optimization
Shiping Gao, Hongzhan Chen, Xiaojun Quan +2
Process reward models (PRMs) provide fine-grained supervision for reasoning, but reliable PRMs often require step annotations or heavy verification pipelines, making them costly to…
Stabilizing Policy Optimization via Logits Convexity
Hongzhan Chen, Tao Yang, Yuhua Zhu +3
While reinforcement learning (RL) has been central to the recent success of large language models (LLMs), RL optimization is notoriously unstable, especially when compared to super…
Discriminative Policy Optimization for Token-Level Reward Models
Hongzhan Chen, Tao Yang, Shiping Gao +4
Process reward models (PRMs) provide more nuanced supervision compared to outcome reward models (ORMs) for optimizing policy models, positioning them as a promising approach to enh…
SocialBench: Sociality Evaluation of Role-Playing Conversational Agents
Hongzhan Chen, Hehong Chen, Ming Yan +8
Large language models (LLMs) have advanced the development of various AI conversational agents, including role-playing conversational agents that mimic diverse characters and human…
Small LLMs Are Weak Tool Learners: A Multi-LLM Agent
Weizhou Shen, Chenliang Li, Hongzhan Chen +5
Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs, empowering them to interact with external tools (e.g., APIs, functions) and complete var…
Knowledge Distillation of Black-Box Large Language Models
Hongzhan Chen, Ruijun Chen, Yuqi Yi +4
Given the exceptional performance of proprietary large language models (LLMs) like GPT-4, recent research has increasingly focused on boosting the capabilities of smaller models th…
MCC-KD: Multi-CoT Consistent Knowledge Distillation
Hongzhan Chen, Siyue Wu, Xiaojun Quan +3
Large language models (LLMs) have showcased remarkable capabilities in complex reasoning through chain of thought (CoT) prompting. Recently, there has been a growing interest in tr…
AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression
Siyue Wu, Hongzhan Chen, Xiaojun Quan +2
Knowledge distillation has attracted a great deal of interest recently to compress pre-trained language models. However, existing knowledge distillation methods suffer from two lim…