6 papers
Glyph: Scaling Context Windows via Visual-Text Compression
Jiale Cheng, Yusen Liu, Xinyu Zhang +11
Large language models (LLMs) increasingly rely on long-context modeling for tasks such as document understanding, code analysis, and multi-step reasoning. However, scaling context…
AndroidGen: Building an Android Language Agent under Data Scarcity
Hanyu Lai, Junjie Gao, Xiao Liu +4
Large language models have opened up a world of possibilities for various NLP tasks, sparking optimism for the future. Despite their potential, LLMs have yet to be widely used as a…
VPO: Aligning Text-to-Video Generation Models with Prompt Optimization
Jiale Cheng, Ruiliang Lyu, Xiaotao Gu +9
Video generation models have achieved remarkable progress in text-to-video tasks. These models are typically trained on text-video pairs with highly detailed and carefully crafted…
Does RLHF Scale? Exploring the Impacts From Data, Model, and Method
Zhenyu Hou, Pengfan Du, Yilin Niu +7
This study explores the scaling properties of Reinforcement Learning from Human Feedback (RLHF) in Large Language Models (LLMs). Although RLHF is considered an important step in po…
VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation
Jiazheng Xu, Yu Huang, Jiale Cheng +19
Visual generative models have achieved remarkable progress in synthesizing photorealistic images and videos, yet aligning their outputs with human preferences across critical dimen…
SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models
Jiale Cheng, Xiao Liu, Cunxiang Wang +7
Instruction-following is a fundamental capability of language models, requiring the model to recognize even the most subtle requirements in the instructions and accurately reflect…