2 citations · 2 across the 2 of their papers we have counts for
5 papers
On Group Relative Policy Optimization Collapse in Agent Search: The Lazy Likelihood-Displacement
Wenlong Deng, Yushu Li, Boying Gong +3
Tool-integrated (TI) reinforcement learning (RL) enables large language models (LLMs) to perform multi-step reasoning by interacting with external tools such as search engines and…
Enhancing Clinical Multiple-Choice Questions Benchmarks with Knowledge Graph Guided Distractor Generation
Running Yang, Wenlong Deng, Minghui Chen +2
Clinical tasks such as diagnosis and treatment require strong decision-making abilities, highlighting the importance of rigorous evaluation benchmarks to assess the reliability of…
On the Effect of Negative Gradient in Group Relative Deep Reinforcement Optimization
Wenlong Deng, Yi Ren, Muchen Li +3
Reinforcement learning (RL) has become popular in enhancing the reasoning capabilities of large language models (LLMs), with Group Relative Policy Optimization (GRPO) emerging as a…
MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs
Juncheng Wu, Wenlong Deng, Xingxuan Li +12
Medical tasks such as diagnosis and treatment planning require precise and complex reasoning, particularly in life-critical domains. Unlike mathematical reasoning, medical reasonin…
Can Textual Gradient Work in Federated Learning?
Minghui Chen, Ruinan Jin, Wenlong Deng +4
Recent studies highlight the promise of LLM-based prompt optimization, especially with TextGrad, which automates differentiation'' via texts and backpropagates textual feedback. Th…