About US

Satori (悟り) is a Japanese term meaning "sudden enlightenment" or "awakening." The Satori team is dedicated to the pursuit of Artificial General Intelligence (AGI), with a particular focus on enhancing the reasoning capabilities of large language models (LLMs)—a crucial step toward this ultimate goal.

Along this journey, the Satori team has released two major research contributions:

• Satori (ICML 2025): Released concurrently with DeepSeek-R1, we propose a novel post-training paradigm that enables LLMs to performs an extended reasoning process with self-reflection: 1) a small-scale format tuning (FT) stage to internalize certain reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning (RL). Our approach results in Satori, a 7B LLM that achieves state-of-the-art reasoning performance.
• Satori-SWE: This work addresses a particularly challenging domain for LLMs: real-world software engineering (SWE) task. We propose Evolutionary Test-Time Scaling (EvoScale) that treats LLM generation as an evolutionary process. By combining reinforcement learning (RL) training and EvoScale test-time scaling, our 32B model, Satori-SWE-32B, achieves performance comparable to models exceeding 100B parameters, while requiring only a small number of samples.

Resources

If you are interested in our work, please refer to our blog and research paper for more technical details!

• Blog
• Satori
• Satori-SWE

Citation

If you find our model and data helpful, please cite our paper:

Satori

@misc{shen2025satorireinforcementlearningchainofactionthought,
      title={Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search}, 
      author={Maohao Shen and Guangtao Zeng and Zhenting Qi and Zhang-Wei Hong and Zhenfang Chen and Wei Lu and Gregory Wornell and Subhro Das and David Cox and Chuang Gan},
      year={2025},
      eprint={2502.02508},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.02508}, 
}

Satori-SWE

@misc{zeng2025satorisweevolutionarytesttimescaling,
      title={Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering}, 
      author={Guangtao Zeng and Maohao Shen and Delin Chen and Zhenting Qi and Subhro Das and Dan Gutfreund and David Cox and Gregory Wornell and Wei Lu and Zhang-Wei Hong and Chuang Gan},
      year={2025},
      eprint={2505.23604},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.23604}, 
}