5 papers
LiteLong: Resource-Efficient Long-Context Data Synthesis for LLMs
Junlong Jia, Xing Wu, Chaochen Gao +8
High-quality long-context data is essential for training large language models (LLMs) capable of processing extensive documents, yet existing synthesis approaches using relevance-b…
Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem Solving
Xinji Mai, Haotian Xu, Zhong-Zhi Li +5
Large Language Models (LLMs) often struggle with mathematical reasoning tasks requiring precise, verifiable computation. While Reinforcement Learning (RL) from outcome-based reward…
From System 1 to System 2: A Survey of Reasoning Large Language Models
Zhong-Zhi Li, Duzhen Zhang, Ming-Liang Zhang +18
Achieving human-level intelligence requires refining the transition from the fast, intuitive System 1 to the slower, more deliberate System 2 reasoning. While System 1 excels in qu…
TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression
Zhong-Zhi Li, Xiao Liang, Zihao Tang +11
Large Language Models (LLMs) have recently achieved remarkable progress by leveraging Reinforcement Learning and extended Chain-of-Thought (CoT) techniques. However, the challenge…
RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems?
Haotian Xu, Xing Wu, Weinong Wang +11
Can scaling transform reasoning? In this work, we explore the untapped potential of scaling Long Chain-of-Thought (Long-CoT) data to 1000k samples, pioneering the development of a…