RRepoGEO

REPOGEO REPORT · LITE

opendilab/awesome-multi-modal-reinforcement-learning

Default branch main · commit 5655e244 · scanned 6/8/2026, 10:47:43 AM

GitHub: 608 stars · 24 forks

AI VISIBILITY SCORE
15 /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
0 / 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 opendilab/awesome-multi-modal-reinforcement-learning, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    awesome-list, multi-modal, reinforcement-learning, mmrl, research-papers, deep-learning, ai, machine-learning
  • highreadme#2
    Explicitly state 'awesome list' in the README's opening sentence

    Why:

    CURRENT
    This is a collection of research papers for **Multi-Modal reinforcement learning (MMRL)**.
    COPY-PASTE FIX
    This is an **awesome list** and a continually updated collection of research papers for **Multi-Modal reinforcement learning (MMRL)**.
  • mediumhomepage#3
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/opendilab/awesome-multi-modal-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 opendilab/awesome-multi-modal-reinforcement-learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv.org
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv.org · recommended 1×
  2. Google Scholar · recommended 1×
  3. NeurIPS · recommended 1×
  4. ICML · recommended 1×
  5. ICLR · recommended 1×
  • CATEGORY QUERY
    Where can I find recent research papers on multi-modal reinforcement learning agents?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. NeurIPS
    4. ICML
    5. ICLR
    6. CVPR
    7. Robotics: Science and Systems (RSS)
    8. International Conference on Robotics and Automation (ICRA)

    AI recommended 8 alternatives but never named opendilab/awesome-multi-modal-reinforcement-learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest advancements in training AI agents using both visual and linguistic data?
    you: not recommended
    AI recommended (in order):
    1. GPT-4V
    2. Google Gemini
    3. Llama 2
    4. LLaVA
    5. CLIP
    6. RT-2
    7. Habitat
    8. ALFRED
    9. Matterport3D
    10. R2R

    AI recommended 10 alternatives but never named opendilab/awesome-multi-modal-reinforcement-learning. 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 opendilab/awesome-multi-modal-reinforcement-learning?
    pass
    AI did not name opendilab/awesome-multi-modal-reinforcement-learning — 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 opendilab/awesome-multi-modal-reinforcement-learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name opendilab/awesome-multi-modal-reinforcement-learning — 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?

  • In one sentence, what problem does the repo opendilab/awesome-multi-modal-reinforcement-learning solve, and who is the primary audience?
    pass
    AI did not name opendilab/awesome-multi-modal-reinforcement-learning — 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?

Embed your GEO score

Drop this badge into the README of opendilab/awesome-multi-modal-reinforcement-learning. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
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