6 papers
MatchLM2Lite: A Scalable MLLM-to-Lite Framework for Reproduced Content Identification
Xiaotian Fan, Hiok Hian Ong, David Yuchen Wang +3
Content moderation is critical for online video platforms to ensure content safety, protect creators, and sustain positive user experiences. Beyond filtering harmful content, platf…
On-Policy Adversarial Flow Distillation for Autoregressive Video Generation
Yang Luo, Shengju Qian, Xiaohang Tang +4
Autoregressive video generators are attractive for streaming, long-horizon, and interactive applications, but distilling strong black-box teachers into causal students remains diff…
Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning
Yong Liu, Zirui Zhu, Chaoyu Gong +3
While fine-tuning large language models (LLMs) for specific tasks often yields impressive results, it comes at the cost of memory inefficiency due to back-propagation in gradient-b…
POME: Post Optimization Model Edit via Muon-style Projection
Yong Liu, Di Fu, Yang Luo +4
We introduce Post-Optimization Model Edit (POME), a new algorithm that enhances the performance of fine-tuned large language models using only their pretrained and fine-tuned check…
MERIT: Maximum-normalized Element-wise Ratio for Language Model Large-batch Training
Yang Luo, Zangwei Zheng, Ziheng Qin +3
Large-batch training has become a cornerstone in accelerating the training of deep neural networks, yet it poses challenges in optimization and generalization. Existing optimizers…
How Does the Textual Information Affect the Retrieval of Multimodal In-Context Learning?
Yang Luo, Zangwei Zheng, Zirui Zhu +1
The increase in parameter size of multimodal large language models (MLLMs) introduces significant capabilities, particularly in-context learning, where MLLMs enhance task performan…