policy-gradient/GRPO-Zero

• hightopics#1

  Add relevant topics to improve categorization

  原因：

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  llm, reinforcement-learning, policy-gradient, deepseek, grpo, low-memory, single-gpu, pytorch, from-scratch

  待办完成忽略
• highreadme#2

  Reposition README opening to highlight lightweight, efficient LLM RL training

  原因：

  当前

  # GRPO:Zero
  
  GRPO training with minimal dependencies (and low GPU memory usage!). We implement almost everything from scratch and only depend on `tokenizers` for tokenization and `pytorch` for training. 
  - No `transformers` and `vLLM` dependencies! 
  - The default config is set to run on a single A40 GPU (48GB VRAM) for a few hours to get good results. (An A40 costs `$0.44` per hour if you rent it from RunPod.)
  - We also support training with a 24GB VRAM GPU (e.g., an RTX 4090 GPU) by offloading the optimizer to CPU. Fortunately, this only adds a small overhead to the training because we only update the policy network a few hundred times during the entire training process.

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  # GRPO:Zero
  
  **Train Large Language Models (LLMs) with Group Relative Policy Optimization (GRPO) from scratch, designed for minimal dependencies and low GPU memory usage.** This repository provides an efficient, pure PyTorch implementation of DeepSeek R1's GRPO algorithm, specifically optimized for single-GPU setups (including 24GB VRAM GPUs like the RTX 4090) and completely free of `transformers` and `vLLM` dependencies. Ideal for researchers and practitioners seeking a lightweight, high-performance solution for LLM reinforcement learning.

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• mediumhomepage#3

  Add a homepage URL

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  https://github.com/policy-gradient/GRPO-Zero

  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 good options for training LLMs using reinforcement learning without heavy transformer dependencies?

  你：未被推荐
  AI 推荐顺序：

  1. RLlib (ray-project/ray)
  2. Stable Baselines3 (DLR-RM/stable-baselines3)
  3. Hugging Face Transformers (huggingface/transformers)
  4. PyTorch (pytorch/pytorch)
  5. TensorFlow (tensorflow/tensorflow)
  6. Acme (deepmind/acme)

  AI 推荐了 6 个替代方案，却始终没点名 policy-gradient/GRPO-Zero。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  How can I efficiently train large language models with policy gradients on a single 24GB GPU?

  你：未被推荐
  AI 推荐顺序：

  1. Hugging Face Transformers
  2. PEFT
  3. LoRA
  4. QLoRA
  5. DeepSpeed ZeRO-2/3
  6. FlashAttention-2
  7. PyTorch FSDP
  8. bitsandbytes
  9. Axolotl
  10. TRL
  11. OpenRLHF

  AI 推荐了 11 个替代方案，却始终没点名 policy-gradient/GRPO-Zero。这就是要补上的差距。

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• Metadata completeness

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  建议：

• README presence

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• Compared to common alternatives in this category, what is the core differentiator of policy-gradient/GRPO-Zero?

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  ✓AI 明确点名了 policy-gradient/GRPO-Zero

  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 policy-gradient/GRPO-Zero in production, what risks or prerequisites should they evaluate first?

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  ✓AI 明确点名了 policy-gradient/GRPO-Zero

  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 policy-gradient/GRPO-Zero solve, and who is the primary audience?

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  ✓AI 明确点名了 policy-gradient/GRPO-Zero

  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？