13 citations · 19 across the 19 of their papers we have counts for
19 papers
Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding
Wenkai Wang, Xiyun Li, Hongcan Guo +5
Graphical User Interface (GUI) grounding requires mapping natural language instructions to precise pixel coordinates. However, due to visually homogeneous elements and dense layout…
Can LLMs Guide Their Own Exploration? Gradient-Guided Reinforcement Learning for LLM Reasoning
Zhenwen Liang, Sidi Lu, Wenhao Yu +4
Reinforcement learning has become essential for strengthening the reasoning abilities of large language models, yet current exploration mechanisms remain fundamentally misaligned w…
MotionEdit: Benchmarking and Learning Motion-Centric Image Editing
Yixin Wan, Lei Ke, Wenhao Yu +2
We introduce MotionEdit, a novel dataset for motion-centric image editing-the task of modifying subject actions and interactions while preserving identity, structure, and physical…
Guided Self-Evolving LLMs with Minimal Human Supervision
Wenhao Yu, Zhenwen Liang, Chengsong Huang +4
AI self-evolution has long been envisioned as a path toward superintelligence, where models autonomously acquire, refine, and internalize knowledge from their own learning experien…
Understanding and Enhancing Mamba-Transformer Hybrids for Memory Recall and Language Modeling
Hyunji Lee, Wenhao Yu, Hongming Zhang +4
Hybrid models that combine state space models (SSMs) with attention mechanisms have shown strong performance by leveraging the efficiency of SSMs and the high recall ability of att…
Don't Throw Away Your Pretrained Model
Shangbin Feng, Wenhao Yu, Yike Wang +3
Alignment training has tradeoffs: it helps language models (LMs) gain in reasoning and instruction following but might lose out on skills such as creativity and calibration, where…
Parallel-R1: Towards Parallel Thinking via Reinforcement Learning
Tong Zheng, Hongming Zhang, Wenhao Yu +7
Parallel thinking has emerged as a novel approach for enhancing the reasoning capabilities of large language models (LLMs) by exploring multiple reasoning paths concurrently. Howev…
Self-Rewarding Vision-Language Model via Reasoning Decomposition
Zongxia Li, Wenhao Yu, Chengsong Huang +8
Vision-Language Models (VLMs) often suffer from visual hallucinations: generating things that are not consistent with visual inputs and language shortcuts, where they skip the visu…
R-Zero: Self-Evolving Reasoning LLM from Zero Data
Chengsong Huang, Wenhao Yu, Xiaoyang Wang +6
Self-evolving Large Language Models (LLMs) offer a scalable path toward super-intelligence by autonomously generating, refining, and learning from their own experiences. However, e…
MobileGUI-RL: Advancing Mobile GUI Agent through Reinforcement Learning in Online Environment
Yucheng Shi, Wenhao Yu, Zaitang Li +5
Recently, there has been a surge of vision-based GUI agents designed to automate everyday mobile and web tasks. These agents interpret raw GUI screenshots and autonomously decide w…
WebEvolver: Enhancing Web Agent Self-Improvement with Coevolving World Model
Tianqing Fang, Hongming Zhang, Zhisong Zhang +4
Agent self-improvement, where the backbone Large Language Model (LLM) of the agent are trained on trajectories sampled autonomously based on their own policies, has emerged as a pr…
WebRollback: Enhancing Web Agents with Explicit Rollback Mechanisms
Zhisong Zhang, Tianqing Fang, Kaixin Ma +4
With recent advancements in large language models, web agents have been greatly improved. However, dealing with complex and dynamic web environments requires more advanced planning…
OpenCharacter: Training Customizable Role-Playing LLMs with Large-Scale Synthetic Personas
Xiaoyang Wang, Hongming Zhang, Tao Ge +3
Customizable role-playing in large language models (LLMs), also known as character generalization, is gaining increasing attention for its versatility and cost-efficiency in develo…
RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph
Siru Ouyang, Wenhao Yu, Kaixin Ma +6
Large Language Models (LLMs) excel in code generation yet struggle with modern AI software engineering tasks. Unlike traditional function-level or file-level coding tasks, AI softw…
LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
Di Wu, Hongwei Wang, Wenhao Yu +3
Recent large language model (LLM)-driven chat assistant systems have integrated memory components to track user-assistant chat histories, enabling more accurate and personalized re…
Leopard: A Vision Language Model For Text-Rich Multi-Image Tasks
Mengzhao Jia, Wenhao Yu, Kaixin Ma +6
Text-rich images, where text serves as the central visual element guiding the overall understanding, are prevalent in real-world applications, such as presentation slides, scanned…
Scaling Synthetic Data Creation with 1,000,000,000 Personas
Tao Ge, Xin Chan, Xiaoyang Wang +3
We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully expl…
Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning
Zhihan Zhang, Tao Ge, Zhenwen Liang +5
Supervised fine-tuning enhances the problem-solving abilities of language models across various mathematical reasoning tasks. To maximize such benefits, existing research focuses o…
MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn Interactions
Zhenwen Liang, Dian Yu, Wenhao Yu +4
Large language models (LLMs) have demonstrated impressive capabilities in mathematical problem solving, particularly in single turn question answering formats. However, real world…