BytedTsinghua-SIA/DAPO

• hightopics#1

  Add relevant topics to the repository

  Why:

  COPY-PASTE FIX

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

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• highreadme#2

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

  Why:

  CURRENT

  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.

  COPY-PASTE FIX

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  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.

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• highlicense#3

  Add a LICENSE file to the repository

  Why:

  CURRENT

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

  COPY-PASTE FIX

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  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.

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

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

  you: not recommended
  AI recommended (in order):

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

  AI recommended 8 alternatives but never named BytedTsinghua-SIA/DAPO. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

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

  you: not recommended
  AI recommended (in order):

  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 recommended 16 alternatives but never named BytedTsinghua-SIA/DAPO. This is the gap to close.

  Show full AI answer

• Metadata completeness

  fail

  Suggestion:

• README presence

  pass

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

  pass
  ✓AI named BytedTsinghua-SIA/DAPO explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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

  pass
  ✓AI named BytedTsinghua-SIA/DAPO explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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

  pass
  ✓AI named BytedTsinghua-SIA/DAPO explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?