RRepoGEO

REPOGEO REPORT · LITE

RLHFlow/Online-RLHF

Default branch main · commit 3a00ed83 · scanned 6/1/2026, 12:27:48 AM

GitHub: 545 stars · 48 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 RLHFlow/Online-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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that aligns with the project's goals for reuse and contribution.
  • hightopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    llama3, llm, rlhf
    COPY-PASTE FIX
    llama3, llm, rlhf, online-rlhf, iterative-dpo, llm-alignment, open-source-llm, deep-reinforcement-learning
  • mediumreadme#3
    Strengthen the README's opening to emphasize 'open-source recipe' and 'online iterative' distinction

    Why:

    CURRENT
    TL;DL: this is a repo to align the large language models (LLMs) by online iterative RLHF. Also check out our technical report and Huggingface Repo!
    COPY-PASTE FIX
    TL;DL: This repository provides a detailed, open-source recipe for aligning Large Language Models (LLMs) through online iterative Reinforcement Learning from Human Feedback (RLHF) and DPO, filling a critical gap in existing offline-focused frameworks. Also check out our technical report and Huggingface Repo!

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 RLHFlow/Online-RLHF
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/trl
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/trl · recommended 2×
  2. deepmind/acme · recommended 1×
  3. OpenAccess-AI-Collective/axolotl · recommended 1×
  4. OpenAI's GPT-4 · recommended 1×
  5. Anthropic's Claude · recommended 1×
  • CATEGORY QUERY
    How to align large language models using online iterative human feedback methods?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face TRL (huggingface/trl)
    2. DeepMind's Acme (deepmind/acme)
    3. Axolotl (OpenAccess-AI-Collective/axolotl)
    4. OpenAI's GPT-4
    5. Anthropic's Claude
    6. LightTag
    7. Argilla (argilla-io/argilla)
    8. scikit-learn (scikit-learn/scikit-learn)

    AI recommended 8 alternatives but never named RLHFlow/Online-RLHF. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an open-source recipe for online iterative DPO to improve LLM performance.
    you: not recommended
    AI recommended (in order):
    1. trl (huggingface/trl)
    2. DeepSpeed (microsoft/DeepSpeed)
    3. RLHF-V (RLHF-V/RLHF-V)
    4. OpenAssistant (OpenAssistant/oasst-data)
    5. Open-Orca (Open-Orca/OpenOrca)
    6. Lit-GPT (Lightning-AI/lit-gpt)
    7. Ax (facebook/Ax)

    AI recommended 7 alternatives but never named RLHFlow/Online-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 RLHFlow/Online-RLHF?
    pass
    AI named RLHFlow/Online-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 RLHFlow/Online-RLHF in production, what risks or prerequisites should they evaluate first?
    pass
    AI named RLHFlow/Online-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 RLHFlow/Online-RLHF solve, and who is the primary audience?
    pass
    AI named RLHFlow/Online-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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RLHFlow/Online-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