12 citations · 12 across the 8 of their papers we have counts for
9 papers
Claim-Level Rubric Rewards for Video Caption Reinforcement Learning
Mingqi Gao, Hongyuan Dong, Yifei Chen +6
In this paper, we introduce Claim-Level Rubric Rewards (CuRe), a structured reward framework designed to address the reward-design bottleneck in reinforcement learning for dense vi…
ViSurf: Visual Supervised-and-Reinforcement Fine-Tuning for Large Vision-and-Language Models
Yuqi Liu, Liangyu Chen, Jiazhen Liu +4
Post-training Large Vision-and-Language Models (LVLMs) typically involves Supervised Fine-Tuning (SFT) for knowledge injection or Reinforcement Learning with Verifiable Rewards (RL…
MGM-Omni: Scaling Omni LLMs to Personalized Long-Horizon Speech
Chengyao Wang, Zhisheng Zhong, Bohao Peng +7
We present MGM-Omni, a unified Omni LLM for omni-modal understanding and expressive, long-horizon speech generation. Unlike cascaded pipelines that isolate speech synthesis, MGM-Om…
ARPO:End-to-End Policy Optimization for GUI Agents with Experience Replay
Fanbin Lu, Zhisheng Zhong, Shu Liu +2
Training large language models (LLMs) as interactive agents for controlling graphical user interfaces (GUIs) presents a unique challenge to optimize long-horizon action sequences w…
VisionReasoner: Unified Reasoning-Integrated Visual Perception via Reinforcement Learning
Yuqi Liu, Tianyuan Qu, Zhisheng Zhong +4
Large vision-language models exhibit inherent capabilities to handle diverse visual perception tasks. In this paper, we introduce VisionReasoner, a unified framework capable of rea…
STEVE: A Step Verification Pipeline for Computer-use Agent Training
Fanbin Lu, Zhisheng Zhong, Ziqin Wei +3
Developing AI agents to autonomously manipulate graphical user interfaces is a long challenging task. Recent advances in data scaling law inspire us to train computer-use agents wi…