749 citations · 1.3k across the 36 of their papers we have counts for
9 papers · 1 filter
Mid-Training Language Models on Raw Video
Jaedong Hwang, Xiaoqian Shen, Ernie Chang +9
Multimodal large language models learn mostly from paired image-text data or annotated video, and raw web video is rarely used to further train an existing language model. We study…
Scaling Laws for Looped Mixture of Experts
Yanbei Chen, Anirudh Goyal, Raghuraman Krishnamoorthi
Looped transformers and Mixture-of-Experts (MoE) offer complementary routes to efficient scaling: recurrence increases computational depth at fixed parameters, while MoE sparsity e…
MobileMoE: Scaling On-Device Mixture of Experts
Yanbei Chen, Hanxian Huang, Ernie Chang +5
Mixture-of-Experts (MoE) has become the de facto architecture for hundred-billion-parameter language models, yet its advantages at sub-billion scales for on-device deployment remai…
WinQ: Accelerating Quantization-Aware Training of Language Models Around Saddle Points
Dongyue Li, Zechun Liu, Kai Yi +6
Quantization-aware training (QAT) is widely adopted to quantize language models by training full-precision weights using gradients from the quantized model. The main bottleneck is…
MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment
Hanxian Huang, Igor Fedorov, Andrey Gromov +14
Real-time AI experiences call for on-device large language models (OD-LLMs) optimized for efficient deployment on resource-constrained hardware. The most useful OD-LLMs produce nea…
dTRPO: Trajectory Reduction in Policy Optimization of Diffusion Large Language Models
Wenxuan Zhang, Lemeng Wu, Changsheng Zhao +11
Diffusion Large Language Models (dLLMs) introduce a new paradigm for language generation, which in turn presents new challenges for aligning them with human preferences. In this wo…
Small Vision-Language Models are Smart Compressors for Long Video Understanding
Junjie Fei, Jun Chen, Zechun Liu +13
Adapting Multimodal Large Language Models (MLLMs) for hour-long videos is bottlenecked by context limits. Dense visual streams saturate token budgets and exacerbate the lost-in-the…
Efficient Universal Perception Encoder
Chenchen Zhu, Saksham Suri, Cijo Jose +8
Running AI models on smart edge devices can unlock versatile user experiences, but presents challenges due to limited compute and the need to handle multiple tasks simultaneously.…
VideoAuto-R1: Video Auto Reasoning via Thinking Once, Answering Twice
Shuming Liu, Mingchen Zhuge, Changsheng Zhao +20
Chain-of-thought (CoT) reasoning has emerged as a powerful tool for multimodal large language models on video understanding tasks. However, its necessity and advantages over direct…