RRepoGEO

REPOGEO REPORT · LITE

OpenLMLab/MOSS-RLHF

Default branch main · commit 4865d826 · scanned 5/24/2026, 3:13:02 PM

GitHub: 1,427 stars · 105 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 OpenLMLab/MOSS-RLHF, 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 README's opening to clarify project purpose

    Why:

    CURRENT
    # MOSS-RLHF

**Congratulations**🎉🎉🎉 We received **the best paper award** at NIPS 2023 Workshop on Instruction Tuning and Instruction Following!
    COPY-PASTE FIX
    # MOSS-RLHF: Unveiling the Secrets of RLHF in Large Language Models (PPO & Reward Modeling)

This repository provides the official code and datasets for our research on Reinforcement Learning from Human Feedback (RLHF), focusing on PPO and reward model training for large language model alignment. We were honored to receive the best paper award at NIPS 2023 Workshop on Instruction Tuning and Instruction Following for "Secrets of RLHF in Large Language Models Part I: PPO".
  • mediumhomepage#2
    Add homepage URL to repository About section

    Why:

    COPY-PASTE FIX
    https://openlmlab.github.io/MOSS-RLHF/
  • lowtopics#3
    Expand repository topics for better specificity

    Why:

    CURRENT
    ai-safety, alignment, rlhf
    COPY-PASTE FIX
    ai-safety, alignment, rlhf, ppo, reward-modeling, large-language-models

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 OpenLMLab/MOSS-RLHF
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 2×
  2. Hugging Face Transformers · recommended 1×
  3. TRL · recommended 1×
  4. DeepSpeed-Chat · recommended 1×
  5. RLlib · recommended 1×
  • CATEGORY QUERY
    How can I apply reinforcement learning with human feedback to improve large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Hugging Face Transformers
    3. TRL
    4. DeepSpeed-Chat
    5. RLlib
    6. Pytorch-Lightning
    7. Keras
    8. Argilla
    9. Label Studio

    AI recommended 9 alternatives but never named OpenLMLab/MOSS-RLHF. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for training a reward model for large language model alignment?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face's TRL (Transformers Reinforcement Learning)
    2. Microsoft DeBERTa-v3-large
    3. Facebook RoBERTa-large
    4. PyTorch
    5. TensorFlow
    6. Hugging Face Transformers library
    7. Vowpal Wabbit
    8. OpenAssistant's OAPhi
    9. GPT-4
    10. Claude Opus
    11. OpenAI API
    12. Anthropic API

    AI recommended 12 alternatives but never named OpenLMLab/MOSS-RLHF. 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 OpenLMLab/MOSS-RLHF?
    pass
    AI named OpenLMLab/MOSS-RLHF explicitly

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

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