REPOGEO REPORT · LITE
policy-gradient/GRPO-Zero
Default branch main · commit d41bb486 · scanned 6/28/2026, 1:32:46 PM
GitHub: 1,868 stars · 94 forks
Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface policy-gradient/GRPO-Zero, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.
Action plan — copy-paste fixes
2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- highreadme#1Reposition README opening to clarify its category as an RLHF framework
Why:
CURRENT# 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.
COPY-PASTE FIX# GRPO:Zero: A PyTorch RLHF Framework for LLMs GRPO:Zero is a PyTorch-based Reinforcement Learning from Human Feedback (RLHF) framework for Large Language Models (LLMs), implementing DeepSeek R1's GRPO algorithm from scratch. It focuses on minimal dependencies and low GPU memory usage, avoiding heavy libraries like `transformers` and `vLLM` to provide efficient policy gradient training.
- mediumhomepage#2Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIXhttps://github.com/policy-gradient/GRPO-Zero
Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash
Category visibility — the real GEO test
Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?
Same questions for every model — switch tabs to compare answers and rankings.
- DeepSpeed · recommended 1×
- Hugging Face Accelerate · recommended 1×
- PyTorch FSDP · recommended 1×
- Megatron-LM · recommended 1×
- bitsandbytes · recommended 1×
- CATEGORY QUERYHow to train large language models using policy gradient with minimal GPU memory?you: not recommendedAI recommended (in order):
- DeepSpeed
- Hugging Face Accelerate
- PyTorch FSDP
- Megatron-LM
- bitsandbytes
- LoRA/QLoRA
- FlashAttention
AI recommended 7 alternatives but never named policy-gradient/GRPO-Zero. This is the gap to close.
Show full AI answer
- CATEGORY QUERYLooking for a PyTorch-based RLHF framework that avoids large transformer library dependencies.you: not recommendedAI recommended (in order):
- TRL
- Hugging Face's RLHF Library
- DeepSpeed-Chat
- Custom PyTorch Implementation
- RLlib
AI recommended 5 alternatives but never named policy-gradient/GRPO-Zero. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
Self-mention check
Does AI even know your repo exists when asked about it directly?
- Compared to common alternatives in this category, what is the core differentiator of policy-gradient/GRPO-Zero?passAI named policy-gradient/GRPO-Zero explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts policy-gradient/GRPO-Zero in production, what risks or prerequisites should they evaluate first?passAI named policy-gradient/GRPO-Zero 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 policy-gradient/GRPO-Zero solve, and who is the primary audience?passAI named policy-gradient/GRPO-Zero explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
Drop this badge into the README of policy-gradient/GRPO-Zero. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
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policy-gradient/GRPO-Zero — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite