12 papers
ANDES: Agent Native Data Evolving Synthesis Tool for Autonomous Instruction Alignment
Zhengyang Zhao, Shengjie Ye, Lu Ma +3
AI agents are increasingly being tasked with automating AI research itself, particularly the critical post-training phase that transforms base LLMs into aligned assistants. However…
OpenWorldLib: A Unified Codebase and Definition of Advanced World Models
DataFlow Team, Bohan Zeng, Daili Hua +39
World models have garnered significant attention as a promising research direction in artificial intelligence, yet a clear and unified definition remains lacking. In this paper, we…
FlipVQA: Scaling Multi-modal Instruction Tuning via Textbook-to-Knowledge Synthesis
Zhen Hao Wong, Jingwen Deng, Yuzhao Wang +6
Textbooks are among the richest repositories of human-verified reasoning knowledge, yet their complex layouts contain multi-column typesetting, cross-page question answer separatio…
Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions
Lu Ma, Hao Liang, Meiyi Qiang +9
Recent advances in large language model (LLM) reasoning have shown that sophisticated behaviors such as planning and self-reflection can emerge through reinforcement learning (RL).…
MathMixup: Boosting LLM Mathematical Reasoning with Difficulty-Controllable Data Synthesis and Curriculum Learning
Xuchen Li, Jing Chen, Xuzhao Li +4
In mathematical reasoning tasks, the advancement of Large Language Models (LLMs) relies heavily on high-quality training data with clearly defined and well-graded difficulty levels…
Unlocking the Potential of Difficulty Prior in RL-based Multimodal Reasoning
Mingrui Chen, Haogeng Liu, Hao Liang +3
In this work, we investigate how explicitly modeling problem's difficulty prior information shapes the effectiveness of reinforcement learning based fine-tuning for multimodal reas…