6 papers
Back on Track: Aligning Rewards and States for Reasoning in Diffusion Large Language Models
Yawen Shao, Jie Xiao, Kai Zhu +6
Reinforcement learning (RL) holds immense promise for enhancing the reasoning capabilities of diffusion large language models (dLLMs). However, progress is fundamentally constraine…
VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection
Huilin Deng, Hongchen Luo, Wei Zhai +2
Zero-shot anomaly detection (ZSAD) recognizes and localizes anomalies in previously unseen objects by establishing feature mapping between textual prompts and inspection images, de…
IIB-LPO: Latent Policy Optimization via Iterative Information Bottleneck
Huilin Deng, Hongchen Luo, Yue Zhu +8
Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) for Large Language Model (LLM) reasoning have been hindered by a persistent challenge: exploration collapse…
GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding
Yawen Shao, Wei Zhai, Yuhang Yang +3
Open-Vocabulary 3D object affordance grounding aims to anticipate ``action possibilities'' regions on 3D objects with arbitrary instructions, which is crucial for robots to generic…
Boosting the Generalization and Reasoning of Vision Language Models with Curriculum Reinforcement Learning
Huilin Deng, Ding Zou, Rui Ma +3
While state-of-the-art vision-language models (VLMs) have demonstrated remarkable capabilities in complex visual-text tasks, their success heavily relies on massive model scaling,…
Leverage Task Context for Object Affordance Ranking
Haojie Huang, Hongchen Luo, Wei Zhai +2
Intelligent agents accomplish different tasks by utilizing various objects based on their affordance, but how to select appropriate objects according to task context is not well-ex…