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README.md

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-Edit this `README.md` markdown file to author your organization card.
+# **Introduction**
+We aim to advance LLM reasoning with autoregressive search capabilities, i.e., a single LLM performs an extended reasoning process with self-reflection and self-exploration of new strategies.
+We achieve this through our proposed Chain-of-Action-Thought (COAT) reasoning and a new post-training paradigm: 1) a small-scale format tuning (FT) stage to internalize the COAT reasoning format and 2) a large-scale self-improvement
+stage leveraging reinforcement learning (RL). Our approach results in Satori, a 7B LLM trained on open-source model (Qwen-2.5-Math-7B) and open-source data (OpenMathInstruct-2 and NuminaMath). Key features of Satori include:
+ - Capable of self-reflection and self-exploration without external guidance.
+ - Achieve state-of-the-art reasoning performance mainly through self-improvement (RL).
+ - Exhibit transferability of reasoning capabilities on unseen domains beyond math.
+
+# **Resources**
+Please refer to our blog and research paper for more details of Satori.
+ - [Blog](https://satori-reasoning.github.io/blog/satori/)
+ - [Paper](https://satori-reasoning.github.io/blog/satori/)
+
+# **Citation**
+If you find our model and data helpful, please consider citing our paper:
+```
+@article{TBD,
+  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},
+  journal={arXiv preprint arXiv: TBD},
+  year={2025}
+}
+```