RRepoGEO

REPOGEO REPORT · LITE

PRIME-RL/PRIME

Default branch main · commit 18ad596f · scanned 5/15/2026, 7:12:57 PM

GitHub: 1,857 stars · 112 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 PRIME-RL/PRIME, 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

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening to clearly state the project's purpose for LLM reasoning

    Why:

    CURRENT
    The README starts with `# Process Reinforcement Through Implicit Rewards`.
    COPY-PASTE FIX
    Add the following sentence immediately after the main title (e.g., after the `div align="center"` block or the H1): "PRIME is a scalable reinforcement learning framework specifically designed to enhance the advanced reasoning capabilities of large language models by leveraging implicit rewards."
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2502.01456
  • lowtopics#3
    Add more specific topics to improve categorization

    Why:

    CURRENT
    llm, reasoning, rl
    COPY-PASTE FIX
    llm, reasoning, rl, reinforcement-learning-for-llms, advanced-reasoning, implicit-rewards, scalable-rl

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 PRIME-RL/PRIME
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. huggingface/peft · recommended 1×
  3. OpenAI GPT-4 · recommended 1×
  4. Anthropic Claude 3 · recommended 1×
  5. thu-ml/tianshou · recommended 1×
  • CATEGORY QUERY
    How can I improve large language model reasoning abilities using reinforcement learning techniques?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face PEFT (huggingface/peft)
    3. OpenAI GPT-4
    4. Anthropic Claude 3
    5. Tianshou (thu-ml/tianshou)
    6. Stable Baselines3 (DLR-RM/stable-baselines3)
    7. Anthropic Claude
    8. d3rlpy (takuseno/d3rlpy)

    AI recommended 8 alternatives but never named PRIME-RL/PRIME. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a scalable reinforcement learning framework to enhance language model performance and reasoning capabilities.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. TRL library (huggingface/trl)
    3. Ray RLlib (ray-project/ray)
    4. DeepMind Acme (deepmind/acme)
    5. OpenAI Baselines (openai/baselines)
    6. CleanRL (vwxyzjn/cleanrl)

    AI recommended 6 alternatives but never named PRIME-RL/PRIME. 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 PRIME-RL/PRIME?
    pass
    AI named PRIME-RL/PRIME explicitly

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

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