4 citations · 9 across the 9 of their papers we have counts for
10 papers
The Choice of Divergence: A Neglected Key to Mitigating Diversity Collapse in Reinforcement Learning with Verifiable Reward
Long Li, Zhijian Zhou, Jiaran Hao +9
A central paradox in fine-tuning Large Language Models (LLMs) with Reinforcement Learning with Verifiable Reward (RLVR) is the frequent degradation of multi-attempt performance (Pa…
AURORA:Automated Training Framework of Universal Process Reward Models via Ensemble Prompting and Reverse Verification
Xiaoyu Tan, Tianchu Yao, Chao Qu +8
The reasoning capabilities of advanced large language models (LLMs) like o1 have revolutionized artificial intelligence applications. Nevertheless, evaluating and optimizing comple…
Refine Knowledge of Large Language Models via Adaptive Contrastive Learning
Yinghui Li, Haojing Huang, Jiayi Kuang +7
How to alleviate the hallucinations of Large Language Models (LLMs) has always been the fundamental goal pursued by the LLMs research community. Looking through numerous hallucinat…
SCP-116K: A High-Quality Problem-Solution Dataset and a Generalized Pipeline for Automated Extraction in the Higher Education Science Domain
Dakuan Lu, Xiaoyu Tan, Rui Xu +5
Recent breakthroughs in large language models (LLMs) exemplified by the impressive mathematical and scientific reasoning capabilities of the o1 model have spotlighted the critical…
Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interaction
Xiaoyu Tan, Yongxin Deng, Chao Qu +4
Recently, models for user representation learning have been widely applied in click-through-rate (CTR) and conversion-rate (CVR) prediction. Usually, the model learns a universal u…
CogniDual Framework: Self-Training Large Language Models within a Dual-System Theoretical Framework for Improving Cognitive Tasks
Yongxin Deng, Xihe Qiu, Xiaoyu Tan +5
Cognitive psychology investigates perception, attention, memory, language, problem-solving, decision-making, and reasoning. Kahneman's dual-system theory elucidates the human decis…
Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Bootstrapped Prolog-based Chain-of-Thought
Xiaoyu Tan, Yongxin Deng, Xihe Qiu +7
Large language models have demonstrated remarkable capabilities as general-purpose assistants, excelling in a wide range of reasoning tasks and supporting various aspects of daily…
Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models
Xihe Qiu, Haoyu Wang, Xiaoyu Tan +6
Effective collaboration in multi-agent systems requires communicating goals and intentions between agents. Current agent frameworks often suffer from dependencies on single-agent e…
Subequivariant Reinforcement Learning Framework for Coordinated Motion Control
Haoyu Wang, Xiaoyu Tan, Xihe Qiu +1
Effective coordination is crucial for motion control with reinforcement learning, especially as the complexity of agents and their motions increases. However, many existing methods…
PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching
Zhenting Qi, Xiaoyu Tan, Shaojie Shi +3
Instruction fine-tuning has conventionally been employed to adapt Large Language Models (LLMs) to a variety of tasks. Nonetheless, this technique often necessitates substantial com…