25 citations · 40 across the 23 of their papers we have counts for
25 papers
Information-Time Proximal Policy Optimization
Yongcheng Zeng, Xinyu Cui, Yan Song +9
RLVR has substantially improved the reasoning capabilities of LLMs. However, existing methods typically parameterize temporal progression in the Markov Decision Process by token-by…
Remember and Reweight: Enhancing Multi-Agent Debate with Experience Memory and Confidence Estimation
Xuanfa Jin, Zhijian Ma, Yongcheng Zeng +3
Multi-agent debate (MAD) improves the reasoning capabilities of large language models by having multiple agents iteratively refine their responses through discussion. However, MAD…
UniMem: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams
Siyu Xia, Chenheng Zhang, Yanting Wu +8
Memory is essential for LLM agents to accumulate task experience and reuse task-specific execution strategies. However, real-world deployment over boundary-agnostic and evolving ta…
-StepNFT: Wider Space Needs Finer Steps in Online RL for Flow-based VLAs
Siting Wang, Xiaofeng Wang, Zheng Zhu +7
Flow-based vision-language-action (VLA) models excel in embodied control but suffer from intractable likelihoods during multi-step sampling, hindering online reinforcement learning…
Swimming Under Constraints: A Safe Reinforcement Learning Framework for Quadrupedal Bio-Inspired Propulsion
Xinyu Cui, Fei Han, Hang Xu +9
Bio-inspired aquatic propulsion offers high thrust and maneuverability but is prone to destabilizing forces such as lift fluctuations, which are further amplified by six-degree-of-…
Sim2Sea: Sim-to-Real Policy Transfer for Maritime Vessel Navigation in Congested Waters
Xinyu Cui, Xuanfa Jin, Xue Yan +7
Autonomous navigation in congested maritime environments is a critical capability for a wide range of real-world applications. However, it remains an unresolved challenge due to co…
Skill-Pro: Learning Reusable Skills from Experience via Non-Parametric PPO for LLM Agents
Qirui Mi, Zhijian Ma, Mengyue Yang +4
LLM-driven agents excel at sequential decision-making but often rely on on-the-fly reasoning, re-deriving solutions even in recurring scenarios. This insufficient experience reuse…
From Experience to Strategy: Empowering LLM Agents with Trainable Graph Memory
Siyu Xia, Zekun Xu, Jiajun Chai +7
Large Language Models (LLMs) based agents have demonstrated remarkable potential in autonomous task-solving across complex, open-ended environments. A promising approach for improv…
Self-Verifying Reflection Helps Transformers with CoT Reasoning
Zhongwei Yu, Wannian Xia, Xue Yan +4
Advanced large language models (LLMs) frequently reflect in reasoning chain-of-thoughts (CoTs), where they self-verify the correctness of current solutions and explore alternatives…
CurES: From Gradient Analysis to Efficient Curriculum Learning for Reasoning LLMs
Yongcheng Zeng, Zexu Sun, Bokai Ji +7
Curriculum learning plays a crucial role in enhancing the training efficiency of large language models (LLMs) on reasoning tasks. However, existing methods often fail to adequately…
Memory-Driven Self-Improvement for Decision Making with Large Language Models
Xue Yan, Zijing Ou, Mengyue Yang +4
Large language models (LLMs) have emerged as effective action policies for sequential decision-making (SDM) tasks due to their extensive prior knowledge. However, this broad yet ge…
Proactive Constrained Policy Optimization with Preemptive Penalty
Ning Yang, Pengyu Wang, Guoqing Liu +3
Safe Reinforcement Learning (RL) often faces significant issues such as constraint violations and instability, necessitating the use of constrained policy optimization, which seeks…
GTA: Grouped-head latenT Attention
Luoyang Sun, Cheng Deng, Jiwen Jiang +5
Attention mechanisms underpin the success of large language models (LLMs), yet their substantial computational and memory overhead poses challenges for optimizing efficiency and pe…
EconGym: A Scalable AI Testbed with Diverse Economic Tasks
Qirui Mi, Qipeng Yang, Zijun Fan +7
Artificial intelligence (AI) has become a powerful tool for economic research, enabling large-scale simulation and policy optimization. However, applying AI effectively requires si…
Enhancing Efficiency and Propulsion in Bio-mimetic Robotic Fish through End-to-End Deep Reinforcement Learning
Xinyu Cui, Boai Sun, Yi Zhu +5
Aquatic organisms are known for their ability to generate efficient propulsion with low energy expenditure. While existing research has sought to leverage bio-inspired structures t…
PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing
Cheng Deng, Luoyang Sun, Jiwen Jiang +10
While scaling laws have been continuously validated in large language models (LLMs) with increasing model parameters, the inherent tension between the inference demands of LLMs and…
Evolving LLMs' Self-Refinement Capability via Synergistic Training-Inference Optimization
Yongcheng Zeng, Xinyu Cui, Xuanfa Jin +11
Self-Refinement refers to a model's ability to revise its own responses to produce improved outputs. This capability can also serve as a fundamental mechanism for Self-Improvement,…
Efficient Reinforcement Learning with Large Language Model Priors
Xue Yan, Yan Song, Xidong Feng +4
In sequential decision-making (SDM) tasks, methods like reinforcement learning (RL) and heuristic search have made notable advances in specific cases. However, they often require e…
AI-Olympics: Exploring the Generalization of Agents through Open Competitions
Chen Wang, Yan Song, Shuai Wu +4
Between 2021 and 2023, AI-Olympics, a series of online AI competitions was hosted by the online evaluation platform Jidi in collaboration with the IJCAI committee. In these competi…
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf
Xuanfa Jin, Ziyan Wang, Yali Du +3
Communication is a fundamental aspect of human society, facilitating the exchange of information and beliefs among people. Despite the advancements in large language models (LLMs),…
Token-level Direct Preference Optimization
Yongcheng Zeng, Guoqing Liu, Weiyu Ma +3
Fine-tuning pre-trained Large Language Models (LLMs) is essential to align them with human values and intentions. This process often utilizes methods like pairwise comparisons and…
Learning Macroeconomic Policies through Dynamic Stackelberg Mean-Field Games
Qirui Mi, Zhiyu Zhao, Chengdong Ma +5
Macroeconomic outcomes emerge from individuals' decisions, making it essential to model how agents interact with macro policy via consumption, investment, and labor choices. We for…
Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach
Weiyu Ma, Qirui Mi, Yongcheng Zeng +5
StarCraft II is a challenging benchmark for AI agents due to the necessity of both precise micro level operations and strategic macro awareness. Previous works, such as Alphastar a…
Ask more, know better: Reinforce-Learned Prompt Questions for Decision Making with Large Language Models
Xue Yan, Yan Song, Xinyu Cui +4
Large language models (LLMs) demonstrate their promise in tackling complicated practical challenges by combining action-based policies with chain of thought (CoT) reasoning. Having…