An Empirical Study on Eliciting and Improving R1-like Reasoning Models
Authors: name: Zhipeng Chen, name: Yingqian Min, name: Beichen Zhang, name: Jie Chen, name: Jinhao Jiang, name: Daixuan Cheng, name: Wayne Xin Zhao, name: Zheng Liu, name: Xu Miao, name: Yang Lu, name: Lei Fang, name: Zhongyuan Wang, name: Ji-Rong Wen

In this report, we present the third technical report on the development of
slow-thinking models as part of the STILL project. As the technical pathway
becomes clearer, scaling RL training has become a central technique for
implementing such reasoning models. We systematically experiment with and
document the effects of various factors influencing RL training, conducting
experiments on both base models and fine-tuned models. Specifically, we
demonstrate that our RL training approach consistently improves the Qwen2.5-32B
base models, enhancing both response length and test accuracy. Furthermore, we
show that even when a model like DeepSeek-R1-Distill-Qwen-1.5B has already
achieved a high performance level, it can be further refined through RL
training, reaching an accuracy of 39.33% on AIME 2024. Beyond RL training, we
also explore the use of tool manipulation, finding that it significantly boosts
the reasoning performance of large reasoning models. This approach achieves a
remarkable accuracy of 86.67% with greedy search on AIME 2024, underscoring its
effectiveness in enhancing model capabilities. We release our resources at the
STILL project website: https://github.com/RUCAIBox/Slow_Thinking_with_LLMs.