4 papers
How Far Can Unsupervised RLVR Scale LLM Training?
Bingxiang He, Yuxin Zuo, Zeyuan Liu +18
Unsupervised reinforcement learning with verifiable rewards (URLVR) offers a pathway to scale LLM training beyond the supervision bottleneck by deriving rewards without ground trut…
MeanCache: From Instantaneous to Average Velocity for Accelerating Flow Matching Inference
Huanlin Gao, Ping Chen, Fuyuan Shi +11
We present MeanCache, a training-free caching framework for efficient Flow Matching inference. Existing caching methods reduce redundant computation but typically rely on instantan…
LeMiCa: Lexicographic Minimax Path Caching for Efficient Diffusion-Based Video Generation
Huanlin Gao, Ping Chen, Fuyuan Shi +5
We present LeMiCa, a training-free and efficient acceleration framework for diffusion-based video generation. While existing caching strategies primarily focus on reducing local he…
HiMo-CLIP: Modeling Semantic Hierarchy and Monotonicity in Vision-Language Alignment
Ruijia Wu, Ping Chen, Fei Shen +8
Contrastive vision-language models like CLIP have achieved impressive results in image-text retrieval by aligning image and text representations in a shared embedding space. Howeve…