RRepoGEO

REPOGEO REPORT · LITE

openai/prm800k

Default branch main · commit 7ecc7947 · scanned 5/24/2026, 6:23:15 AM

GitHub: 2,133 stars · 126 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

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 openai/prm800k, 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.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's introductory paragraph to clarify purpose and audience

    Why:

    CURRENT
    This repository accompanies the paper Let's Verify Step by Step and presents the PRM800K dataset introduced there. PRM800K is a process supervision dataset containing 800,000 step-level correctness labels for model-generated solutions to problems from the MATH dataset. More information on PRM800K and the project can be found in the paper.
    COPY-PASTE FIX
    This repository provides the PRM800K dataset, a crucial resource for AI researchers and developers aiming to improve large language model performance on complex mathematical problem-solving tasks. PRM800K is a process supervision dataset containing 800,000 step-level correctness labels for model-generated solutions to problems from the MATH dataset, enabling the training of robust reward models for LLM alignment and evaluation. More information on PRM800K and the project can be found in the accompanying paper and blog post.
  • mediumhomepage#2
    Add the project's blog post as the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://openai.com/research/improving-mathematical-reasoning-with-process-supervision

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.

Recall
0 / 2
0% of queries surface openai/prm800k
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. OpenAI API · recommended 1×
  3. Anthropic Claude · recommended 1×
  4. Wolfram Alpha API · recommended 1×
  5. sympy/sympy · recommended 1×
  • CATEGORY QUERY
    How can I improve large language model performance on complex mathematical problem-solving tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. OpenAI API
    3. Anthropic Claude
    4. Wolfram Alpha API
    5. SymPy (sympy/sympy)
    6. NumPy (numpy/numpy)
    7. SciPy (scipy/scipy)
    8. LangChain (langchain-ai/langchain)
    9. LlamaIndex (run-llama/llama_index)
    10. Pinecone
    11. Weaviate (weaviate/weaviate)
    12. ChromaDB (chroma-core/chroma)
    13. Google's Minerva
    14. Meta's Galactica
    15. OpenAI Codex

    AI recommended 15 alternatives but never named openai/prm800k. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find datasets with step-by-step correctness labels for AI model training?
    you: not recommended
    AI recommended (in order):
    1. MATH Dataset
    2. GSM8K
    3. AQuA-RAD
    4. HotpotQA
    5. StrategyQA
    6. ProofWriter
    7. DROP

    AI recommended 7 alternatives but never named openai/prm800k. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

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 openai/prm800k?
    pass
    AI named openai/prm800k explicitly

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

  • If a team adopts openai/prm800k in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/prm800k 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 openai/prm800k solve, and who is the primary audience?
    pass
    AI named openai/prm800k explicitly

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

Embed your GEO score

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MARKDOWN (README)
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openai/prm800k — 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