- 🎉 [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**!
- 🛠️ [2025-06-04] How to process feedback data in the "era of experience"? We propose [Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement Fine-Tuning of LLMs](https://arxiv.org/abs/2505.17826), which leverages Data-Juicer for its data pipelines tailored for RFT scenarios.
- 🎉 [2025-06-04] Our [Data-Model Co-development Survey](https://ieeexplore.ieee.org/document/11027559) has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (**TPAMI**)! Welcome to explore and contribute the [awesome-list](https://datajuicer.github.io/data-juicer/en/main/docs/awesome_llm_data.html).
- 🔎 [2025-06-04] We introduce [DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?](https://www.arxiv.org/abs/2505.16915) A synthetic benchmark revealing notable performance drops despite large models' proficiency with short descriptions.
- 🎉 [2025-05-06] Our work of [Data-Juicer Sandbox](https://arxiv.org/abs/2407.11784) has been accepted as a **ICML'25 Spotlight** (top 2.6% of all submissions)!
- 💡 [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.
- [2025-01-03] We support post-tuning scenarios better, via 20+ related new [OPs](https://github.com/datajuicer/data-juicer/releases/tag/v1.0.2), and via unified [dataset format](https://github.com/datajuicer/data-juicer/releases/tag/v1.0.3) compatible to LLaMA-Factory and ModelScope-Swift.
- [2024-12-17] We propose *HumanVBench*, which comprises 16 human-centric tasks with synthetic data, benchmarking 22 video-MLLMs' capabilities from views of inner emotion and outer manifestations. See more details in our [paper](https://arxiv.org/abs/2412.17574), and try to [evaluate](https://github.com/datajuicer/data-juicer/tree/HumanVBench) your models with it.
- [2024-11-22] We release DJ [v1.0.0](https://github.com/datajuicer/data-juicer/releases/tag/v1.0.0), in which we refactored Data-Juicer's *Operator*, *Dataset*, *Sandbox* and many other modules for better usability, such as supporting fault-tolerant, FastAPI and adaptive resource management.
- [2024-08-25] We give a [tutorial](https://datajuicer.github.io/data-juicer/_static/tutorial_kdd24.html) about data processing for multimodal LLMs in KDD'2024.
- [2024-08-09] We propose Img-Diff, which enhances the performance of multimodal large language models through *contrastive data synthesis*, achieving a score that is 12 points higher than GPT-4V on the [MMVP benchmark](https://tsb0601.github.io/mmvp_blog/). See more details in our [paper](https://arxiv.org/abs/2408.04594), and download the dataset from [huggingface](https://huggingface.co/datasets/datajuicer/Img-Diff) and [modelscope](https://modelscope.cn/datasets/Data-Juicer/Img-Diff).
- [2024-07-24] "Tianchi Better Synth Data Synthesis Competition for Multimodal Large Models" — Our 4th data-centric LLM competition has kicked off! Please visit the competition's [official website](https://tianchi.aliyun.com/competition/entrance/532251) for more information.
- [2024-07-17] We utilized the Data-Juicer [Sandbox Laboratory Suite](https://datajuicer.github.io/data-juicer-sandbox/en/main/index.html) to systematically optimize data and models through a co-development workflow between data and models, achieving a new top spot on the [VBench](https://huggingface.co/spaces/Vchitect/VBench_Leaderboard) text-to-video leaderboard. The related achievements have been compiled and published in a [paper](http://arxiv.org/abs/2407.11784), and the model has been released on the [ModelScope](https://modelscope.cn/models/Data-Juicer/Data-Juicer-T2V) and [HuggingFace](https://huggingface.co/datajuicer/Data-Juicer-T2V) platforms.
- [2024-07-12] Our *awesome list of MLLM-Data* has evolved into a systemic [survey](https://arxiv.org/abs/2407.08583) from model-data co-development perspective. Welcome to [explore](docs/awesome_llm_data.md) and contribute!
- [2024-06-01] ModelScope-Sora "Data Directors" creative sprint—Our third data-centric LLM competition has kicked off! Please visit the competition's [official website](https://tianchi.aliyun.com/competition/entrance/532219) for more information.
- [2024-03-07] We release **Data-Juicer [v0.2.0](https://github.com/datajuicer/data-juicer/releases/tag/v0.2.0)** now! 
In this new version, we support more features for **multimodal data (including video now)**, and introduce **[DJ-SORA](docs/DJ_SORA.md)** to provide open large-scale, high-quality datasets for SORA-like models.
- [2024-02-20] We have actively maintained an *awesome list of LLM-Data*, welcome to [visit](docs/awesome_llm_data.md) and contribute!
- [2024-02-05] Our paper has been accepted by SIGMOD'24 industrial track!
- [2024-01-10] Discover new horizons in "Data Mixture"—Our second data-centric LLM competition has kicked off! Please visit the competition's [official website](https://tianchi.aliyun.com/competition/entrance/532174) for more information.
- [2024-01-05] We release **Data-Juicer v0.1.3** now!
In this new version, we support **more Python versions** (3.8-3.10), and support **multimodal** dataset [converting](tools/fmt_conversion/multimodal/README.md)/[processing](docs/Operators.md) (Including texts, images, and audios. More modalities will be supported in the future).
Besides, our paper is also updated to [v3](https://arxiv.org/abs/2309.02033).
- [2023-10-13] Our first data-centric LLM competition begins! Please
  visit the competition's official websites, FT-Data Ranker ([1B Track](https://tianchi.aliyun.com/competition/entrance/532157), [7B Track](https://tianchi.aliyun.com/competition/entrance/532158)), for more information.