RRepoGEO

REPOGEO REPORT · LITE

openai/weak-to-strong

Default branch main · commit 6b450f2c · scanned 5/15/2026, 4:23:05 AM

GitHub: 2,555 stars · 312 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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/weak-to-strong, 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
  • highabout#1
    Add a concise 'About' description

    Why:

    COPY-PASTE FIX
    Code for implementing weak-to-strong generalization, a technique for training stronger AI models using supervision from weaker, human-supervisable models, with applications in AI safety and scalable oversight.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    weak-to-strong-generalization, ai-safety, scalable-oversight, machine-learning, language-models, vision-models, deep-learning, research
  • mediumhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2312.09391

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/weak-to-strong
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 1×
  2. TensorFlow/Keras · recommended 1×
  3. Hugging Face Transformers library · recommended 1×
  4. Knowledge Distillation Toolkit (KDT) · recommended 1×
  5. huggingface/transformers · recommended 1×
  • CATEGORY QUERY
    How can I enhance a smaller model's performance using a more powerful teacher model?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow/Keras
    3. Hugging Face Transformers library
    4. Knowledge Distillation Toolkit (KDT)

    AI recommended 4 alternatives but never named openai/weak-to-strong. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks exist for training language models with labels from another language model?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. Accelerate (huggingface/accelerate)
    5. OpenNMT
    6. PaddlePaddle (PaddlePaddle/Paddle)
    7. PaddleNLP (PaddlePaddle/PaddleNLP)

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

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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/weak-to-strong?
    pass
    AI named openai/weak-to-strong 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/weak-to-strong in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/weak-to-strong 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/weak-to-strong solve, and who is the primary audience?
    pass
    AI did not name openai/weak-to-strong — likely talking about a different project

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

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openai/weak-to-strong — 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