BytedTsinghua-SIA/DAPO

• hightopics#1

  Add relevant topics to the repository

  原因：

  复制粘贴的修复

  复制

  ["reinforcement-learning", "llm", "large-language-models", "policy-optimization", "deep-learning", "ai", "machine-learning", "aime-2024"]

  待办完成忽略
• highreadme#2

  Strengthen the README's opening sentence to clarify LLM RL focus

  原因：

  当前

  We release a fully open-sourced system for large-scale LLM RL, including algorithm, code infrastructure, and dataset. The system achieves state-of-the-art large-scale LLM RL performance. We propose the **D**ecoupled Clip and **D**ynamic s**A**mpling **P**olicy **O**ptimization (**DAPO**) algorithm.

  复制粘贴的修复

  复制

  DAPO is a fully open-sourced system specifically designed for **large-scale Reinforcement Learning (RL) with Large Language Models (LLMs)**, encompassing algorithms, code infrastructure, and datasets. It introduces the **D**ecoupled Clip and **D**ynamic s**A**mpling **P**olicy **O**ptimization (**DAPO**) algorithm, achieving state-of-the-art performance in LLM RL.

  待办完成忽略
• highlicense#3

  Add a LICENSE file to the repository

  原因：

  当前

  (no LICENSE file detected — the repo has no recognizable license)

  复制粘贴的修复

  复制

  Create a `LICENSE` file in the repository root. If a specific open-source license is intended (e.g., MIT, Apache-2.0), add its full text. If it's a custom or compound license, explicitly state the terms in the `LICENSE` file and consider adding a brief summary in the README.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  What are the best open-source systems for large-scale reinforcement learning with LLMs?

  你：未被推荐
  AI 推荐顺序：

  1. Hugging Face Transformers
  2. trl
  3. accelerate
  4. DeepMind Acme
  5. Ray RLlib
  6. OpenAI Baselines
  7. Stable Baselines3
  8. PyTorch Lightning

  AI 推荐了 8 个替代方案，却始终没点名 BytedTsinghua-SIA/DAPO。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Seeking robust infrastructure for developing and training large language models with reinforcement learning.

  你：未被推荐
  AI 推荐顺序：

  1. Google Cloud Platform (GCP)
  2. Cloud TPUs
  3. Google Kubernetes Engine (GKE)
  4. Cloud Storage
  5. Vertex AI
  6. AWS (Amazon Web Services)
  7. EC2 P4d Instances
  8. Amazon SageMaker
  9. Microsoft Azure
  10. ND A100 v4-series VMs
  11. Azure Machine Learning
  12. NVIDIA DGX Systems
  13. DGX A100
  14. DGX Cloud
  15. CoreWeave
  16. Lambda Labs Cloud

  AI 推荐了 16 个替代方案，却始终没点名 BytedTsinghua-SIA/DAPO。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  fail

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of BytedTsinghua-SIA/DAPO?

  pass
  ✓AI 明确点名了 BytedTsinghua-SIA/DAPO

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts BytedTsinghua-SIA/DAPO in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 BytedTsinghua-SIA/DAPO

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo BytedTsinghua-SIA/DAPO solve, and who is the primary audience?

  pass
  ✓AI 明确点名了 BytedTsinghua-SIA/DAPO

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？