Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and
  Beyond
Authors: name: Liang Wen, name: Yunke Cai, name: Fenrui Xiao, name: Xin He, name: Qi An, name: Zhenyu Duan, name: Yimin Du, name: Junchen Liu, name: Lifu Tang, name: Xiaowei Lv, name: Haosheng Zou, name: Yongchao Deng, name: Shousheng Jia, name: Xiangzheng Zhang

This paper presents our work on the Light-R1 series, with models, data, and
code all released.
  We first focus on training long COT models from scratch, specifically
starting from models initially lacking long COT capabilities. Using a
curriculum training recipe consisting of two-stage SFT and semi-on-policy DPO,
we train our model Light-R1-32B from Qwen2.5-32B-Instruct, resulting in
superior math performance compared to DeepSeek-R1-Distill-Qwen-32B. Despite
being trained exclusively on math data, Light-R1-32B shows strong
generalization across other domains. In the subsequent phase of this work, we
highlight the significant benefit of the 3k dataset constructed for the second
SFT stage on enhancing other models. By fine-tuning DeepSeek-R1-Distilled
models using this dataset, we obtain new SOTA models in 7B and 14B, while the
32B model, Light-R1-32B-DS performed comparably to QwQ-32B and DeepSeek-R1.
  Furthermore, we extend our work by applying reinforcement learning,
specifically GRPO, on long-COT models to further improve reasoning performance.
We successfully train our final Light-R1-14B-DS with RL, achieving SOTA
performance among 14B parameter models in math. With AIME24 & 25 scores of 74.0
and 60.2 respectively, Light-R1-14B-DS surpasses even many 32B models and
DeepSeek-R1-Distill-Llama-70B. Its RL training also exhibits well expected
behavior, showing simultaneous increase in response length and reward score.
  The Light-R1 series of work validates training long-COT models from scratch,
showcases the art in SFT data and releases SOTA models from RL.