3 papers
cs.CL2026
UltraLogic: Enhancing LLM Reasoning through Large-Scale Data Synthesis and Bipolar Float Reward
Yile Liu, Yixian Liu, Zongwei Li +7
While Large Language Models (LLMs) have demonstrated significant potential in natural language processing , complex general-purpose reasoning requiring multi-step logic, planning,…
cs.AI2025
Zero Reinforcement Learning Towards General Domains
Yuyuan Zeng, Yufei Huang, Can Xu +5
Zero Reinforcement Learning (Zero-RL) has proven to be an effective approach for enhancing the reasoning capabilities of large language models (LLMs) by directly applying reinforce…
cs.CL2025
Reinforcement Learning on Pre-Training Data
Siheng Li, Kejiao Li, Zenan Xu +33
The growing disparity between the exponential scaling of computational resources and the finite growth of high-quality text data now constrains conventional scaling approaches for…