Create README.md

README.md

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+---
+license: mit
+datasets:
+- agentica-org/DeepScaleR-Preview-Dataset
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+tags:
+- LRM
+- hybrid_reasoning
+- efficient_reasoning
+---
+
+# AdaptThink: LLM Can Learn When to Think
+
+<p align="center">
+    🤗 <a href="https://huggingface.co/collections/THU-KEG/adaptthink-682a1059aa9f5102c4fa0470" target="_blank">HF Collections</a> • 💻 <a href="" target="_blank">Github Repo</a> • 📃 <a href="https://arxiv.org/abs/2505.13417" target="_blank">Paper</a>
+</p>
+
+## 🔍 Table of Contents
+- [🤖️ AdaptThink](#adapt_think)
+- [⚙️ Released Models](#model)
+- [📊 Evaluation](#evaluation)
+- [📝 Citation](#citation)
+
+<a name="adapt_think"></a>
+## 🤖️ AdaptThink
+We present **AdapThink**, a novel reinforcement learning (RL) algorithm that enables reasoning models to adaptively choose between **Thinking** and **NoThinking** modes according to the difficulty of each input problem, thereby achieving automatic hybrid reasoning. Specifically, the model engages in thinking only when the problem is determined to be challenging; for other simple question, it will bypass the thinking process and directly produce a concise final solution. This approach substantially reduces inference costs while further improving overall performance.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66cdd285c51a915bd5f2d017/JaeJiBwLkcwAuexRAkLX5.png)
+
+
+
+<a name="model"></a>
+## ⚙️ Released Models
+
+### All Available Datasets and Models
+We apply the AdaptThink algorithm on DeepSeek-R1-Distill-Qwen-1.5B with $\delta$ from 0 to 0.1, and DeepSeek-R1-Distill-Qwen-7B with $\delta=0.05$. A larger $\large$ results in a higher proportion of NoThinking responses, which reduces more inference costs but also diminish the resultant improvement in accuracy.
+
+All the trained models are available on HuggingFace. 
+
+
+| Name | HF Repo |
+|---|---|
+| AdaptThink-1.5B-delta0 | [🤗 HF Repo](https://huggingface.co/THU-KEG/AdaptThink-1.5B-delta0) |
+| AdaptThink-1.5B-delta0.01 | [🤗 HF Repo](https://huggingface.co/THU-KEG/AdaptThink-1.5B-delta0.01) |
+| AdaptThink-1.5B-delta0.02 | [🤗 HF Repo](https://huggingface.co/THU-KEG/AdaptThink-1.5B-delta0.02) |
+| AdaptThink-1.5B-delta0.05 | [🤗 HF Repo](https://huggingface.co/THU-KEG/AdaptThink-1.5B-delta0.05) |
+| AdaptThink-1.5B-delta0.075 | [🤗 HF Repo](https://huggingface.co/THU-KEG/AdaptThink-1.5B-delta0.075) |
+| AdaptThink-1.5B-delta0.1 | [🤗 HF Repo](https://huggingface.co/THU-KEG/AdaptThink-1.5B-delta0.1) |
+| AdaptThink-7B-delta0.05 | [🤗 HF Repo](https://huggingface.co/THU-KEG/AdaptThink-7B-delta0.05) |
+
+<a name="training"></a>
+
+## 📊 Evaluation Results
+
+We list our evaluation results as follows:
+##### 1.  Comparison with existing methods for efficient reasoning on mathematics datasets
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66cdd285c51a915bd5f2d017/ZLV8ZfEet1dp-4jyzBxiG.png)
+
+##### 2. Nothinking responses ratio and accuracy across different difficulty levels on MATH500
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66cdd285c51a915bd5f2d017/GUNfW9qO2aaT9_lo1XXPf.png)
+
+##### 3. Comparison of different $\delta$ values
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66cdd285c51a915bd5f2d017/RXrXwxVSAYlR3-_t0GUwV.png)
+
+##### 4. Evaluation results on MMLU
+
+<img width="1000" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/66cdd285c51a915bd5f2d017/19K2u6PNmYz3gx3JnHgn4.png">
+
+<a name="citation"></a>
+## 📝 Citation
+
+If you find our work useful, please consider citing LongReward:
+
+```
+@article{zhang2025adapt_think,
+  title = {AdaptThink: LLM Can Learn When to Think} 
+  author={Jiajie Zhang and Nianyi Lin and Lei Hou and Ling Feng and Juanzi Li},
+  journal={arXiv preprint arXiv: 2505.13417},
+  url={https://arxiv.org/abs/2505.13417}
+  year={2025}
+}
+```
+