3 citations · 8 across the 11 of their papers we have counts for
12 papers
Tool-Aware Optimization with Entropy Guidance for Efficient Agentic Reinforcement Learning
Hongye Cao, Nuo Yan, Haoyuan Deng +5
Agentic reinforcement learning (RL) equips large language models (LLMs) with tool-use capabilities that substantially improve reasoning on complex tasks. However, integrating exter…
Efficient Reinforcement Learning with Semantic and Token Entropy for LLM Reasoning
Hongye Cao, Zhixin Bai, Ziyue Peng +5
Reinforcement learning with verifiable rewards (RLVR) has demonstrated superior performance in enhancing the reasoning capability of large language models (LLMs). However, this acc…
Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning
Siyuan Gan, Jiaheng Liu, Boyan Wang +8
Large reasoning models (LRMs) have attracted much attention due to their exceptional performance. However, their performance mainly stems from thinking, a long Chain of Thought (Co…
Multi-Agent Reinforcement Learning with Communication-Constrained Priors
Guang Yang, Tianpei Yang, Jingwen Qiao +4
Communication is one of the effective means to improve the learning of cooperative policy in multi-agent systems. However, in most real-world scenarios, lossy communication is a pr…
MedSentry: Understanding and Mitigating Safety Risks in Medical LLM Multi-Agent Systems
Kai Chen, Taihang Zhen, Hewei Wang +7
As large language models (LLMs) are increasingly deployed in healthcare, ensuring their safety, particularly within collaborative multi-agent configurations, is paramount. In this…
Model-Based Offline Reinforcement Learning with Adversarial Data Augmentation
Hongye Cao, Fan Feng, Jing Huo +4
Model-based offline Reinforcement Learning (RL) constructs environment models from offline datasets to perform conservative policy optimization. Existing approaches focus on learni…
MDTeamGPT: A Self-Evolving LLM-based Multi-Agent Framework for Multi-Disciplinary Team Medical Consultation
Kai Chen, Xinfeng Li, Tianpei Yang +3
Large Language Models (LLMs) have made significant progress in various fields. However, challenges remain in Multi-Disciplinary Team (MDT) medical consultations. Current research e…
Faster Game Solving via Asymmetry of Step Sizes
Linjian Meng, Tianpei Yang, Youzhi Zhang +2
Counterfactual Regret Minimization (CFR) algorithms are widely used to compute a Nash equilibrium (NE) in two-player zero-sum imperfect-information extensive-form games (IIGs). Amo…
Causal Information Prioritization for Efficient Reinforcement Learning
Hongye Cao, Fan Feng, Tianpei Yang +2
Current Reinforcement Learning (RL) methods often suffer from sample-inefficiency, resulting from blind exploration strategies that neglect causal relationships among states, actio…
Towards Empowerment Gain through Causal Structure Learning in Model-Based RL
Hongye Cao, Fan Feng, Meng Fang +4
In Model-Based Reinforcement Learning (MBRL), incorporating causal structures into dynamics models provides agents with a structured understanding of the environments, enabling eff…
SafeDialBench: A Fine-Grained Safety Evaluation Benchmark for Large Language Models in Multi-Turn Dialogues with Diverse Jailbreak Attacks
Hongye Cao, Sijia Jing, Yanming Wang +14
With the rapid advancement of Large Language Models (LLMs), the safety of LLMs has been a critical concern requiring precise assessment. Current benchmarks primarily concentrate on…
Efficient Last-iterate Convergence Algorithms in Solving Games
Linjian Meng, Youzhi Zhang, Zhenxing Ge +6
To establish last-iterate convergence for Counterfactual Regret Minimization (CFR) algorithms in learning a Nash equilibrium (NE) of extensive-form games (EFGs), recent studies ref…