RRepoGEO

REPOGEO REPORT · LITE

openai/lm-human-preferences

Default branch master · commit cbfd210b · scanned 5/19/2026, 6:43:07 AM

GitHub: 1,390 stars · 172 forks

Scan history for this repo

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.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
46 /100
Critical
Category recall
1 / 2
Avg rank #4.0 when recommended
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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/lm-human-preferences, 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 opening paragraph to clarify its historical and archived status

    Why:

    CURRENT
    This repository contains code for the paper Fine-Tuning Language Models from Human Preferences. See also our blog post.
    COPY-PASTE FIX
    This repository contains the foundational research code for the paper Fine-Tuning Language Models from Human Preferences, pioneering the Reinforcement Learning from Human Feedback (RLHF) approach. While archived and provided as-is, it serves as a historical reference for early RLHF implementations. See also our blog post.
  • mediumabout#2
    Enhance the 'About' description to reflect its pioneering and archived status

    Why:

    CURRENT
    Code for the paper Fine-Tuning Language Models from Human Preferences
    COPY-PASTE FIX
    Pioneering research code for Fine-Tuning Language Models from Human Preferences (RLHF), demonstrating training reward models and fine-tuning LMs with human feedback. Archived for historical reference.

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
1 / 2
50% of queries surface openai/lm-human-preferences
Avg rank
#4.0
Lower is better. #1 = top recommendation.
Share of voice
5%
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. huggingface/trl · recommended 1×
  3. OpenAI API · recommended 1×
  4. GPT-4 · recommended 1×
  5. GPT-3.5 · recommended 1×
  • CATEGORY QUERY
    How can I fine-tune a language model using human feedback to improve its outputs?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face TRL (huggingface/trl)
    3. OpenAI API
    4. GPT-4
    5. GPT-3.5
    6. DeepSpeed (microsoft/DeepSpeed)
    7. RLlib (ray-project/ray)
    8. PyTorch Lightning (Lightning-AI/lightning)
    9. PyTorch (pytorch/pytorch)
    10. TensorFlow Agents (tensorflow/agents)
    11. TensorFlow (tensorflow/tensorflow)

    AI recommended 11 alternatives but never named openai/lm-human-preferences. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks exist for training reward models from human preference labels for language models?
    you: #4
    AI recommended (in order):
    1. Hugging Face Transformers
    2. TRL
    3. DeepSpeed-Chat
    4. openai/lm-human-preferences (openai/lm-human-preferences) ← you
    5. trlX
    6. OpenRLHF
    7. Ray RLlib
    8. Stable Baselines3
    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/lm-human-preferences?
    pass
    AI did not name openai/lm-human-preferences — 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?

  • If a team adopts openai/lm-human-preferences in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/lm-human-preferences 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/lm-human-preferences solve, and who is the primary audience?
    pass
    AI did not name openai/lm-human-preferences — 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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite