7 papers
AEGPO: Adaptive Entropy-Guided Policy Optimization for Diffusion Models
Yuming Li, Qingyu Li, Chengyu Bai +6
Reinforcement learning from human feedback (RLHF) shows promise for aligning diffusion and flow models, yet policy optimization methods such as GRPO suffer from inefficient and sta…
INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats
Mengzhao Chen, Meng Wu, Hui Jin +10
Modern AI hardware, such as Nvidia's Blackwell architecture, is increasingly embracing low-precision floating-point (FP) formats to handle the pervasive activation outliers in Larg…
Open-Vocabulary Spatio-Temporal Scene Graph for Robot Perception and Teleoperation Planning
Yi Wang, Zeyu Xue, Mujie Liu +5
Teleoperation via natural-language reduces operator workload and enhances safety in high-risk or remote settings. However, in dynamic remote scenes, transmission latency during bid…
RewardDance: Reward Scaling in Visual Generation
Jie Wu, Yu Gao, Zilyu Ye +9
Reward Models (RMs) are critical for improving generation models via Reinforcement Learning (RL), yet the RM scaling paradigm in visual generation remains largely unexplored. It pr…
WorldWeaver: Generating Long-Horizon Video Worlds via Rich Perception
Zhiheng Liu, Xueqing Deng, Shoufa Chen +7
Generative video modeling has made significant strides, yet ensuring structural and temporal consistency over long sequences remains a challenge. Current methods predominantly rely…
Scaling Law for Quantization-Aware Training
Mengzhao Chen, Chaoyi Zhang, Jing Liu +8
Large language models (LLMs) demand substantial computational and memory resources, creating deployment challenges. Quantization-aware training (QAT) addresses these challenges by…