Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement
  Learning
Authors: name: Tian Xie, name: Zitian Gao, name: Qingnan Ren, name: Haoming Luo, name: Yuqian Hong, name: Bryan Dai, name: Joey Zhou, name: Kai Qiu, name: Zhirong Wu, name: Chong Luo

Inspired by the success of DeepSeek-R1, we explore the potential of
rule-based reinforcement learning (RL) in large reasoning models. To analyze
reasoning dynamics, we use synthetic logic puzzles as training data due to
their controllable complexity and straightforward answer verification. We make
some key technical contributions that lead to effective and stable RL training:
a system prompt that emphasizes the thinking and answering process, a stringent
format reward function that penalizes outputs for taking shortcuts, and a
straightforward training recipe that achieves stable convergence. Our 7B model
develops advanced reasoning skills-such as reflection, verification, and
summarization-that are absent from the logic corpus. Remarkably, after training
on just 5K logic problems, it demonstrates generalization abilities to the
challenging math benchmarks AIME and AMC.