1 citations · 1 across the 9 of their papers we have counts for
9 papers
UP: Unbounded Positive Asymmetric Optimization for Breaking the Exploration-Stability Dilemma
Chongyu Fan, Pengfei Liu, Jingjia Huang +2
Reinforcement learning (RL) has become the standard paradigm for enhancing the complex reasoning capabilities of large language models (LLMs). To achieve sample efficiency, modern…
Context-Aware RL for Agentic and Multimodal LLMs
Peiyang Xu, Bangzheng Li, Sijia Liu +4
Large language models (LLMs) often fail when answering requires identifying a small but decisive piece of evidence within a long or complex context, such as a single line in a tool…
ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation
Yihua Zhang, Mingfu Liang, Jiyan Yang +11
Recent advances in multimodal recommenders excel at feature fusion but remain opaque and inefficient decision-makers, lacking explicit reasoning and self-awareness of uncertainty.…
Beyond SFT: Reinforcement Learning for Safer Large Reasoning Models with Better Reasoning Ability
Jinghan Jia, Nathalie Baracaldo, Sijia Liu
Large reasoning models (LRMs) extend large language models by generating explicit chain-of-thought (CoT) reasoning, significantly improving mathematical and logical problem solving…
EPiC: Towards Lossless Speedup for Reasoning Training through Edge-Preserving CoT Condensation
Jinghan Jia, Hadi Reisizadeh, Chongyu Fan +3
Large language models (LLMs) have shown remarkable reasoning capabilities when trained with chain-of-thought (CoT) supervision. However, the long and verbose CoT traces, especially…
Reasoning Model Unlearning: Forgetting Traces, Not Just Answers, While Preserving Reasoning Skills
Changsheng Wang, Chongyu Fan, Yihua Zhang +5
Recent advances in large reasoning models (LRMs) have enabled strong chain-of-thought (CoT) generation through test-time computation. While these multi-step reasoning capabilities…
CyclicReflex: Improving Reasoning Models via Cyclical Reflection Token Scheduling
Chongyu Fan, Yihua Zhang, Jinghan Jia +2
Large reasoning models (LRMs), such as OpenAI's o1 and DeepSeek-R1, harness test-time scaling to perform multi-step reasoning for complex problem-solving. This reasoning process, e…
BLUR: A Bi-Level Optimization Approach for LLM Unlearning
Hadi Reisizadeh, Jinghan Jia, Zhiqi Bu +6
Enabling large language models (LLMs) to unlearn knowledge and capabilities acquired during training has proven vital for ensuring compliance with data regulations and promoting et…
Robust MRI Reconstruction by Smoothed Unrolling (SMUG)
Shijun Liang, Van Hoang Minh Nguyen, Jinghan Jia +3
As the popularity of deep learning (DL) in the field of magnetic resonance imaging (MRI) continues to rise, recent research has indicated that DL-based MRI reconstruction models mi…