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add paper scripts

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PeterGriffinJin
2025-03-13 13:57:47 +00:00
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README.md
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Through RL (rule-based outcome reward), the 3B **base** LLM (both Qwen2.5-3b-base and Llama3.2-3b-base) develops reasoning and search engine calling abilities all on its own.
Twitter thread: [link](https://x.com/BowenJin13/status/1895544294473109889); Full experiment log: [link](https://wandb.ai/peterjin/Search-R1-open)
Paper: [link](https://arxiv.org/pdf/2503.09516); Model and data: [link](https://huggingface.co/collections/PeterJinGo/search-r1-67d1a021202731cb065740f5); Twitter thread: [link](https://x.com/BowenJin13/status/1895544294473109889); Full experiment log 1: [link](https://wandb.ai/peterjin/Search-R1-open); Full experiment log 2: [link](hhttps://wandb.ai/peterjin/Search-R1-nq_hotpotqa_train/)
Paper: [link](); Model and data: [link](https://huggingface.co/collections/PeterJinGo/search-r1-67d1a021202731cb065740f5);
You can refer to this [link](https://github.com/PeterGriffinJin/Search-R1/tree/main/scripts/nq_hotpotqa) for detailed instructions on reproducing the results from the paper.
The paper will be released soon!
![single-turn](public/single-turn.png)
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The concept of Search-R1 is inspired by [Deepseek-R1](https://github.com/deepseek-ai/DeepSeek-R1) and [TinyZero](https://github.com/Jiayi-Pan/TinyZero/tree/main).
Its implementation is built upon [veRL](https://github.com/volcengine/verl) and [RAGEN](https://github.com/ZihanWang314/RAGEN/tree/main).
We sincerely appreciate the efforts of these teams for their contributions to open-source research and development.
We thank Jinsung Yoon and Sercan Arik for insightful discussions.
## Citations
To be added
```bibtex
@misc{jin2025searchr1,
title = {Search-R1: Train your LLMs to reason and call a search engine with reinforcement learning},