RRepoGEO

REPOGEO REPORT · LITE

openai/lm-human-preferences

Default branch master · commit cbfd210b · scanned 6/30/2026, 2:18:21 PM

GitHub: 1,394 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
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/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
    Clarify the README's opening to emphasize foundational research and historical significance

    Why:

    CURRENT
    # lm-human-preferences
This repository contains code for the paper Fine-Tuning Language Models from Human Preferences. See also our blog post.
    COPY-PASTE FIX
    # lm-human-preferences
This repository contains the foundational research code for the paper Fine-Tuning Language Models from Human Preferences. It represents an early, seminal implementation of Reinforcement Learning from Human Feedback (RLHF) for language models. See also our blog post.
  • mediumreadme#2
    Add a section guiding users to modern RLHF frameworks

    Why:

    COPY-PASTE FIX
    ## Relation to Modern RLHF Frameworks
This repository provides the original research implementation. For actively maintained and more generalized frameworks that build upon these concepts, consider exploring libraries such as Hugging Face TRL (Transformers Reinforcement Learning).

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/lm-human-preferences
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. Hugging Face TRL · recommended 1×
  3. DeepSpeed · recommended 1×
  4. RLlib · recommended 1×
  5. OpenAI Baselines · recommended 1×
  • CATEGORY QUERY
    How can I fine-tune a language model using human preference data effectively?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face TRL
    3. DeepSpeed
    4. RLlib
    5. OpenAI Baselines
    6. Spinning Up in Deep RL
    7. PyTorch Lightning
    8. Keras
    9. AlpacaFarm

    AI recommended 9 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 feedback for NLP tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. TRL library (huggingface/trl)
    3. OpenAI Baselines (openai/baselines)
    4. Acme (deepmind/acme)
    5. OpenSpiel (deepmind/open_spiel)
    6. DeepSpeed-Chat (microsoft/DeepSpeed)
    7. Colossal-AI (hpcaitech/ColossalAI)
    8. PyTorch (pytorch/pytorch)
    9. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    10. Hugging Face Accelerate (huggingface/accelerate)
    11. TensorFlow (tensorflow/tensorflow)
    12. Keras (keras-team/keras)

    AI recommended 12 alternatives but never named openai/lm-human-preferences. 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/lm-human-preferences?
    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?

  • 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 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?

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openai/lm-human-preferences — 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