2 citations · 3 across the 15 of their papers we have counts for
16 papers
Coalition-Aware Skill Reliability for Self-Evolving Agents
Qiyan Zhao, Xiaofeng Zhang, Bo Liu +11
Agent skills, structured artifacts distilled from interaction trajectories and dynamically reused from skill banks, have become a central mechanism for enabling large language mode…
Enhancing Social Intelligence in LLMs with Hierarchical Reasoning and Utterance-Level Goal Rewarding
Xiaofeng Wang, Kakam Chong, Shuai Xiao +9
Large language models (LLMs) excel in structured tasks but struggle with dynamic social interactions, where success requires long-term goal coordination and rapid adaptation. Curre…
P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling
Pinyi Zhang, Ting-En Lin, Yuchuan Wu +7
Personalized alignment of large language models seeks to adapt responses to individual user preferences, typically via reinforcement learning. A key challenge is obtaining accurate…
MOA: Multi-Objective Alignment for Role-Playing Agents
Chonghua Liao, Ke Wang, Yuchuan Wu +3
Role-playing agents (RPAs) require balancing multiple objectives, such as instruction following, persona consistency, and stylistic fidelity, which are not always perfectly aligned…
Empowering RepoQA-Agent based on Reinforcement Learning Driven by Monte-carlo Tree Search
Guochang Li, Yuchen Liu, Zhen Qin +7
Repository-level software engineering tasks require large language models (LLMs) to efficiently navigate and extract information from complex codebases through multi-turn tool inte…
Agentic Reinforcement Learning with Implicit Step Rewards
Xiaoqian Liu, Ke Wang, Yuchuan Wu +4
Large language models (LLMs) are increasingly developed as autonomous agents using reinforcement learning (agentic RL) that reason and act in interactive environments. However, spa…
CPO: Addressing Reward Ambiguity in Role-playing Dialogue via Comparative Policy Optimization
Xinge Ye, Rui Wang, Yuchuan Wu +4
Reinforcement Learning Fine-Tuning (RLFT) has achieved notable success in tasks with objectively verifiable answers (e.g., code generation, mathematical reasoning), yet struggles w…
OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction
Haonan Zhang, Run Luo, Xiong Liu +10
Role-Playing Agents (RPAs), benefiting from large language models, is an emerging interactive AI system that simulates roles or characters with diverse personalities. However, exis…
A Simple "Motivation" Can Enhance Reinforcement Finetuning of Large Reasoning Models
Junjie Zhang, Guozheng Ma, Shunyu Liu +6
Reinforcement Learning with Verifiable Rewards~(RLVR) has emerged as a powerful learn-to-reason paradigm for large reasoning models to tackle complex tasks. However, the current RL…
TimeHC-RL: Temporal-aware Hierarchical Cognitive Reinforcement Learning for Enhancing LLMs' Social Intelligence
Guiyang Hou, Xing Gao, Yuchuan Wu +8
Recently, Large Language Models (LLMs) have made significant progress in IQ-related domains that require careful thinking, such as mathematics and coding. However, enhancing LLMs'…
Supervised Optimism Correction: Be Confident When LLMs Are Sure
Junjie Zhang, Rushuai Yang, Shunyu Liu +5
In this work, we establish a novel theoretical connection between supervised fine-tuning and offline reinforcement learning under the token-level Markov decision process, revealing…
Reverse Preference Optimization for Complex Instruction Following
Xiang Huang, Ting-En Lin, Feiteng Fang +5
Instruction following (IF) is a critical capability for large language models (LLMs). However, handling complex instructions with multiple constraints remains challenging. Previous…
A Survey of Direct Preference Optimization
Shunyu Liu, Wenkai Fang, Zetian Hu +9
Large Language Models (LLMs) have demonstrated unprecedented generative capabilities, yet their alignment with human values remains critical for ensuring helpful and harmless deplo…
SDPO: Segment-Level Direct Preference Optimization for Social Agents
Aobo Kong, Wentao Ma, Shiwan Zhao +7
Social agents powered by large language models (LLMs) can simulate human social behaviors but fall short in handling complex social dialogues. Direct Preference Optimization (DPO)…
EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning
Xiaoqian Liu, Ke Wang, Yongbin Li +6
Large Language Models (LLMs) have shown impressive reasoning capabilities in well-defined problems with clear solutions, such as mathematics and coding. However, they still struggl…
OpenOmni: Advancing Open-Source Omnimodal Large Language Models with Progressive Multimodal Alignment and Real-Time Self-Aware Emotional Speech Synthesis
Run Luo, Ting-En Lin, Haonan Zhang +10
Recent advancements in omnimodal learning have significantly improved understanding and generation across images, text, and speech, yet these developments remain predominantly conf…