Update README.md

README.md

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 - You can try it in the [playground](http://8.138.149.181/) with a managed JupyterLab.
 - More details can be found in our [homepage](https://github.com/modelscope/data-juicer) or [documents](https://modelscope.github.io/data-juicer/en/main/).
 
-## News 
 ## News
 - 🎉 [2025-09-19] Our work of [Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models](https://arxiv.org/abs/2501.14755) has been accepted as a **NeurIPS'25 Spotlight** (top 3.1% of all submissions)!
 - 🎉 [2025-09-19] Our two works regarding data mixture/selection/synthesis: [Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data](https://arxiv.org/abs/2502.04380) and [MindGYM: What Matters in Question Synthesis for Thinking-Centric Fine-Tuning?](https://arxiv.org/abs/2503.09499) have been accepted by **NeurIPS'25**!
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 - 💡 [2025-03-13] We propose [MindGYM: What Matters in Question Synthesis for Thinking-Centric Fine-Tuning?](https://arxiv.org/abs/2503.09499) A new data synthesis method that enables large models to self-synthesize high-quality, low-variance data for efficient fine-tuning, (e.g., *16%* gain on [MathVision](https://mathllm.github.io/mathvision/#leaderboard) using only *400 samples*). 
 - 🤝 [2025-02-28] DJ has been integrated in [Ray's official Ecosystem](https://docs.ray.io/en/latest/ray-overview/ray-libraries.html) and [Example Gallery](https://docs.ray.io/en/latest/ray-more-libs/data_juicer_distributed_data_processing.html). Besides, our patch in DJ2.0 for the streaming JSON reader has been officially integrated by [Apache Arrow](https://github.com/apache/arrow/pull/45084). 
 - 🎉 [2025-02-27] Our work on contrastive data synthesis, [ImgDiff](https://arxiv.org/pdf/2408.04594), has been accepted by **CVPR'25**!
-- 💡 [2025-02-05] We propose a new data selection method, [Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data](https://www.arxiv.org/abs/2502.04380). It is theoretically informed, via treating diversity as a reward, achieves better overall performance across 7 benchmarks when post-training SOTA LLMs. 
-- 🎉 [2025-01-11] We release our 2.0 paper, [Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models](https://arxiv.org/abs/2501.14755). It now can process 70B data samples within 2.1h, using 6400 CPU cores on 50 Ray nodes from Alibaba Cloud cluster, and deduplicate 5TB data within 2.8h using 1280 CPU cores on 8 Ray nodes.
+- 💡 [2025-02-05] We propose a new data selection method, [Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data](https://www.arxiv.org/abs/2502.04380). It is theoretically informed, via treating diversity as a reward, achieves better overall performance across 7 benchmarks when post-training SOTA LLMs.