Update README.md

README.md

@@ -22,110 +22,102 @@ pipeline_tag: image-text-to-text
 </div>
 <hr>
 <div align="center" style="line-height: 1;">
-  <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
-    <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
+  <a href="https://www.turningpoint-ai.com/" target="_blank" style="margin: 2px;">
+    <img alt="Homepage" src="https://img.shields.io/badge/🐳Homepage-TurningPointAI-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
   </a>
-  <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
+  <!-- <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
     <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20R1-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a> -->
+  <a href="https://huggingface.co/turningpoint-ai" target="_blank" style="margin: 2px;">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-TurningPoint%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
   </a>
-  <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
-    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
-
-<div align="center" style="line-height: 1;">
-  <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
+  <!-- <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
     <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
+  </a> -->
+  <!-- <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
     <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
+  </a> -->
   <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
-    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-TurningPoint_AI-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
   </a>
 </div>
 
-<div align="center" style="line-height: 1;">
+<!-- <div align="center" style="line-height: 1;">
   <a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE" style="margin: 2px;">
     <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
   </a>
-</div>
+</div> -->
 
 
 <p align="center">
-  <a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf"><b>Paper Link</b>👁️</a>
+  <a href="https://arxiv.org/pdf/2503.05132"><b>Paper Link</b>👁️</a>
 </p>
 
 
-## 1. Introduction
+## 🚀 Introduction
 
-We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. 
-DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning.
-With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors.
-However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance,
-we introduce DeepSeek-R1, which incorporates cold-start data before RL.
-DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. 
-To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models.
+The recent DeepSeek-R1 demonstrated how reinforcement learning with simple
+rule-based reward can enable autonomous development of complex reasoning in
+large language models, characterized by the "aha moment", in which the model
+manifest self-reflection and increased response length during training. However,
+attempts to extend this success to multimodal reasoning often failed to reproduce
+these key characteristics. In this report, we present the first successful replication
+of these emergent characteristics for multimodal reasoning on only a non-SFT
+2B model. Starting with Qwen2-VL-2B and applying reinforcement learning
+directly on the SAT dataset, our model achieves 59.47% accuracy on CVBench,
+outperforming the base model by approximately ~30% and exceeding both SFT
+setting by ~2%. In addition, we share our failed attempts and insights in attempting
+to achieve R1-like reasoning using RL with instruct models, aiming to shed light on
+the challenges involved. Our key observations include: (1) applying RL on instruct
+model often results in trivial reasoning trajectories, and (2) naive length reward
+are ineffective in eliciting reasoning capabilities. The project code is available at
+https://github.com/turningpoint-ai/VisualThinker-R1-Zero
 
-**NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the [Usage Recommendation](#usage-recommendations) section.**
+<!-- **NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the [Usage Recommendation](#usage-recommendations) section.**
 
 <p align="center">
   <img width="80%" src="figures/benchmark.jpg">
-</p>
-
-## 2. Model Summary
-
----
-
-**Post-Training: Large-Scale Reinforcement Learning on the Base Model**
-
--  We directly apply reinforcement learning (RL) to the base model without relying on supervised fine-tuning (SFT) as a preliminary step. This approach allows the model to explore chain-of-thought (CoT) for solving complex problems, resulting in the development of DeepSeek-R1-Zero. DeepSeek-R1-Zero demonstrates capabilities such as self-verification, reflection, and generating long CoTs, marking a significant milestone for the research community. Notably, it is the first open research to validate that reasoning capabilities of LLMs can be incentivized purely through RL, without the need for SFT. This breakthrough paves the way for future advancements in this area.
+</p> -->
 
--   We introduce our pipeline to develop DeepSeek-R1. The pipeline incorporates two RL stages aimed at discovering improved reasoning patterns and aligning with human preferences, as well as two SFT stages that serve as the seed for the model's reasoning and non-reasoning capabilities.
-    We believe the pipeline will benefit the industry by creating better models. 
+## 🔮 Highlights
+1. We are the **first to successfully produce the emergent “aha moment” and increased response length** for multimodal reasoning on just a **non-SFT 2B model**.
+2. We showed that **vision-centric** tasks could also benefit from improved reasoning capabilities.  
 
----
-
-**Distillation: Smaller Models Can Be Powerful Too**
-
--  We demonstrate that the reasoning patterns of larger models can be distilled into smaller models, resulting in better performance compared to the reasoning patterns discovered through RL on small models. The open source DeepSeek-R1, as well as its API, will benefit the research community to distill better smaller models in the future. 
-- Using the reasoning data generated by DeepSeek-R1, we fine-tuned several dense models that are widely used in the research community. The evaluation results demonstrate that the distilled smaller dense models perform exceptionally well on benchmarks. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community.
-
-## 3. Model Downloads
-
-### DeepSeek-R1 Models
-
-<div align="center">
+Similar to DeepSeek R1, self reflection behavior is also observed during our RL training on vision-centric reasoning tasks. The model exhibits an emergent ability to rethink and correct its mistakes:
 
-| **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
-| :------------: | :------------: | :------------: | :------------: | :------------: |
-| DeepSeek-R1-Zero | 671B | 37B | 128K   | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Zero)   |
-| DeepSeek-R1   | 671B | 37B |  128K   | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1)   |
-
-</div>
-
-DeepSeek-R1-Zero & DeepSeek-R1 are trained based on DeepSeek-V3-Base. 
-For more details regarding the model architecture, please refer to [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repository.
+```
+. . .
+Therefore, dark brown wooden bed with white blanket is not above the doorway.
+But wait! I can think of something else.
+Maybe it's just higher than above the doorway, but slightly lower than above the doorway.
+. . .
+```
+## ⚙️ Requirements and Installation
+* Python >= 3.10
+* Pytorch == 2.0.1
+* CUDA Version >= 11.7
+* Install required packages:
+```bash
+# install transformers
+pip install git+https://github.com/huggingface/transformers
+# install qwen-vl utils
+pip install qwen-vl-utils
+```
 
-### DeepSeek-R1-Distill Models
+## 💻 Model Downloads and Usage
 
-<div align="center">
+```
+# Load model directly
+from transformers import AutoProcessor, AutoModelForImageTextToText
 
-| **Model** | **Base Model** | **Download** |
-| :------------: | :------------: | :------------: |
-| DeepSeek-R1-Distill-Qwen-1.5B  | [Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)   |
-| DeepSeek-R1-Distill-Qwen-7B  | [Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B)   |
-| DeepSeek-R1-Distill-Llama-8B  | [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B)   |
-| DeepSeek-R1-Distill-Qwen-14B   | [Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B)   |
-|DeepSeek-R1-Distill-Qwen-32B  | [Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B) | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B)   |
-| DeepSeek-R1-Distill-Llama-70B  | [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B)   |
+processor = AutoProcessor.from_pretrained("turningpoint-ai/VisualThinker-R1-Zero")
+model = AutoModelForImageTextToText.from_pretrained("turningpoint-ai/VisualThinker-R1-Zero")
 
-</div>
+# Prepare input
 
-DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1.
-We slightly change their configs and tokenizers. Please use our setting to run these models.
+```
 
-## 4. Evaluation Results
+## 📰 Evaluation Results
 
 ### DeepSeek-R1-Evaluation
  For all our models, the maximum generation length is set to 32,768 tokens. For benchmarks requiring sampling, we use a temperature of $0.6$, a top-p value of $0.95$, and generate 64 responses per query to estimate pass@1.
@@ -161,89 +153,33 @@ We slightly change their configs and tokenizers. Please use our setting to run t
 
 </div>
 
+## 🙌 Stay Connected!
 
-### Distilled Model Evaluation
-
-
-<div align="center">
-
-| Model                                    | AIME 2024 pass@1 | AIME 2024 cons@64 | MATH-500 pass@1 | GPQA Diamond pass@1 | LiveCodeBench pass@1 | CodeForces rating |
-|------------------------------------------|------------------|-------------------|-----------------|----------------------|----------------------|-------------------|
-| GPT-4o-0513                          | 9.3              | 13.4              | 74.6            | 49.9                 | 32.9                 | 759               |
-| Claude-3.5-Sonnet-1022             | 16.0             | 26.7                 | 78.3            | 65.0                 | 38.9                 | 717               |
-| o1-mini                              | 63.6             | 80.0              | 90.0            | 60.0                 | 53.8                 | **1820**          |
-| QwQ-32B-Preview                              | 44.0             | 60.0                 | 90.6            | 54.5               | 41.9                 | 1316              |
-| DeepSeek-R1-Distill-Qwen-1.5B       | 28.9             | 52.7              | 83.9            | 33.8                 | 16.9                 | 954               |
-| DeepSeek-R1-Distill-Qwen-7B          | 55.5             | 83.3              | 92.8            | 49.1                 | 37.6                 | 1189              |
-| DeepSeek-R1-Distill-Qwen-14B         | 69.7             | 80.0              | 93.9            | 59.1                 | 53.1                 | 1481              |
-| DeepSeek-R1-Distill-Qwen-32B        | **72.6**         | 83.3              | 94.3            | 62.1                 | 57.2                 | 1691              |
-| DeepSeek-R1-Distill-Llama-8B         | 50.4             | 80.0              | 89.1            | 49.0                 | 39.6                 | 1205              |
-| DeepSeek-R1-Distill-Llama-70B        | 70.0             | **86.7**          | **94.5**        | **65.2**             | **57.5**             | 1633              |
-
-</div>
-
-
-## 5. Chat Website & API Platform
-You can chat with DeepSeek-R1 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com), and switch on the button "DeepThink"
-
-We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/)
-
-## 6. How to Run Locally
+We are always open to engaging discussions, collaborations, or even just sharing a virtual coffee. To get in touch or join our team, visit [TurningPoint AI](https://www.turningpoint-ai.com/)'s homepage for contact information.
 
-### DeepSeek-R1 Models
+## 📖 Acknowledgements
 
-Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running DeepSeek-R1 locally.
+We sincerely thank [DeepSeek](https://github.com/deepseek-ai/DeepSeek-R1), [Open-R1](https://github.com/huggingface/open-r1), [QwenVL](https://github.com/QwenLM/Qwen2.5-VL), [Open-R1-Multimodal](https://github.com/EvolvingLMMs-Lab/open-r1-multimodal), [R1-V](https://github.com/Deep-Agent/R1-V), [SAT](https://arxiv.org/abs/2412.07755), and [CV-Bench](https://cambrian-mllm.github.io/) for providing open source resources that laid the foundation of our project. 
 
-**NOTE: Hugging Face's Transformers has not been directly supported yet.**
+## 🤝 Contributors
 
-### DeepSeek-R1-Distill Models
+Here are the key contributors from [TurningPoint AI](https://www.turningpoint-ai.com/) to this project:
 
-DeepSeek-R1-Distill models can be utilized in the same manner as Qwen or Llama models.
+[Hengguang Zhou](https://hengguangzhou.github.io/)<sup>1</sup><sup>* </sup>, [Xirui Li](https://xirui-li.github.io/)<sup>1</sup><sup>* </sup>, [Ruochen Wang](https://ruocwang.github.io/)<sup>1</sup><sup>† </sup>, [Minhao Cheng](https://cmhcbb.github.io/)<sup>2</sup>, [Tianyi Zhou](https://tianyizhou.github.io/)<sup>3</sup> and [Cho-Jui Hsieh](https://web.cs.ucla.edu/~chohsieh/)<sup>1</sup><sup>4</sup>
 
-For instance, you can easily start a service using [vLLM](https://github.com/vllm-project/vllm):
+<sup>*</sup> Project Leads, <sup>†</sup> Main Advisor
+<sup>1</sup>University of California, Los Angeles, <sup>2</sup>Penn State University, <sup>3</sup>University of Maryland and <sup>4</sup>Google Research
 
-```shell
-vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --tensor-parallel-size 2 --max-model-len 32768 --enforce-eager
+## ✏️ Citation
 ```
-
-You can also easily start a service using [SGLang](https://github.com/sgl-project/sglang)
-
-```bash
-python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --trust-remote-code --tp 2
-```
-
-### Usage Recommendations
-
-**We recommend adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the expected performance:**
-
-1. Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs.
-2. **Avoid adding a system prompt; all instructions should be contained within the user prompt.**
-3. For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}."
-4. When evaluating model performance, it is recommended to conduct multiple tests and average the results.
-
-Additionally, we have observed that the DeepSeek-R1 series models tend to bypass thinking pattern (i.e., outputting "\<think\>\n\n\</think\>") when responding to certain queries, which can adversely affect the model's performance.
-**To ensure that the model engages in thorough reasoning, we recommend enforcing the model to initiate its response with "\<think\>\n" at the beginning of every output.**
-
-## 7. License
-This code repository and the model weights are licensed under the [MIT License](https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE).
-DeepSeek-R1 series support commercial use, allow for any modifications and derivative works, including, but not limited to, distillation for training other LLMs. Please note that:
-- DeepSeek-R1-Distill-Qwen-1.5B, DeepSeek-R1-Distill-Qwen-7B, DeepSeek-R1-Distill-Qwen-14B and DeepSeek-R1-Distill-Qwen-32B are derived from [Qwen-2.5 series](https://github.com/QwenLM/Qwen2.5), which are originally licensed under [Apache 2.0 License](https://huggingface.co/Qwen/Qwen2.5-1.5B/blob/main/LICENSE), and now finetuned with 800k samples curated with DeepSeek-R1.
-- DeepSeek-R1-Distill-Llama-8B is derived from Llama3.1-8B-Base and is originally licensed under [llama3.1 license](https://huggingface.co/meta-llama/Llama-3.1-8B/blob/main/LICENSE).
-- DeepSeek-R1-Distill-Llama-70B is derived from Llama3.3-70B-Instruct and is originally licensed under [llama3.3 license](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct/blob/main/LICENSE).
-
-## 8. Citation
-```
-@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
-      title={DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}, 
-      author={DeepSeek-AI},
+@misc{zhou2025r1zerosahamomentvisual,
+      title={R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model}, 
+      author={Hengguang Zhou and Xirui Li and Ruochen Wang and Minhao Cheng and Tianyi Zhou and Cho-Jui Hsieh},
       year={2025},
-      eprint={2501.12948},
+      eprint={2503.05132},
       archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2501.12948}, 
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2503.05132}, 
 }
 
-```
-
-## 9. Contact
-If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).
+```