Understanding R1-Zero-Like Training: A Critical Perspective
Authors: name: Zichen Liu, name: Changyu Chen, name: Wenjun Li, name: Penghui Qi, name: Tianyu Pang, name: Chao Du, name: Wee Sun Lee, name: Min Lin

DeepSeek-R1-Zero has shown that reinforcement learning (RL) at scale can
directly enhance the reasoning capabilities of LLMs without supervised
fine-tuning. In this work, we critically examine R1-Zero-like training by
analyzing its two core components: base models and RL. We investigate a wide
range of base models, including DeepSeek-V3-Base, to understand how pretraining
characteristics influence RL performance. Our analysis reveals that
DeepSeek-V3-Base already exhibit ''Aha moment'', while Qwen2.5 base models
demonstrate strong reasoning capabilities even without prompt templates,
suggesting potential pretraining biases. Additionally, we identify an
optimization bias in Group Relative Policy Optimization (GRPO), which
artificially increases response length (especially for incorrect outputs)
during training. To address this, we introduce Dr. GRPO, an unbiased
optimization method that improves token efficiency while maintaining reasoning
performance. Leveraging these insights, we present a minimalist R1-Zero recipe
that achieves 43.3% accuracy on AIME 2024 with a 7B base model, establishing a
new state-of-the-art. Our code is available at
https://github.com/sail-sg/understand-r1-zero.