Researchers have explored the efficacy of mid-training large language models on raw video data, without captions or text supervision. By encoding video frames into visual tokens and training a language model to predict the next visual token, they found significant improvements in both video and image benchmarks. The mid-trained Qwen3-1.7B model showed a 2.9-point increase on video tasks and a 5.1-point increase on image tasks compared to a model without this mid-training. Notably, text performance remained stable, and the gains in visual tasks emerged early in the training process. AI
IMPACT This research suggests a novel, self-supervised approach to enhance multimodal LLMs using raw video, potentially reducing reliance on costly text-based annotations.
RANK_REASON The cluster describes a research paper published on arXiv detailing a new method for training language models. [lever_c_demoted from research: ic=1 ai=1.0]
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
