RRepoGEO

REPOGEO REPORT · LITE

EvolvingLMMs-Lab/open-r1-multimodal

Default branch main · commit 232b7ba8 · scanned 5/26/2026, 2:02:57 AM

GitHub: 1,550 stars · 72 forks

AI VISIBILITY SCORE
22 /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
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 EvolvingLMMs-Lab/open-r1-multimodal, 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 specific topics for multimodal RLHF and math reasoning

    Why:

    COPY-PASTE FIX
    multimodal-llm, rlhf, vision-language-models, math-reasoning, reinforcement-learning, deep-learning, transformers, qwen2-vl
  • highreadme#2
    Clarify the README's opening paragraph to state the problem solved

    Why:

    CURRENT
    We conducted a speed-run on to investigate R1's paradigm in multimodal models after observing growing interest in R1 and studying the elegant implementation of the GRPO algorithm in `open-r1` and `trl`.
    COPY-PASTE FIX
    This repository extends the `open-r1` paradigm to enable multimodal large language model (VLM) training with Reinforcement Learning from Human Feedback (RLHF). It provides an implementation for integrating VLMs like Qwen2-VL, alongside open-sourced 8k multimodal RL training examples focused on math reasoning and pre-trained GRPO models, primarily for AI researchers and developers.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://github.com/EvolvingLMMs-Lab/open-r1-multimodal

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 EvolvingLMMs-Lab/open-r1-multimodal
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 3×
  2. huggingface/transformers · recommended 2×
  3. huggingface/trl · recommended 2×
  4. huggingface/peft · recommended 1×
  5. huggingface/alignment-handbook · recommended 1×
  • CATEGORY QUERY
    How to train multimodal large language models using reinforcement learning from human feedback?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PEFT (huggingface/peft)
    3. TRL (huggingface/trl)
    4. Alignment Handbook (huggingface/alignment-handbook)
    5. DeepSpeed (microsoft/DeepSpeed)
    6. RLlib (ray-project/ray)
    7. Ray (ray-project/ray)
    8. OpenAI Baselines (openai/baselines)
    9. Spinning Up (openai/spinningup)
    10. PyTorch Lightning (Lightning-AI/lightning)
    11. Keras (keras-team/keras)

    AI recommended 11 alternatives but never named EvolvingLMMs-Lab/open-r1-multimodal. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools for fine-tuning vision-language models with reinforcement learning for math tasks.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. TRL (huggingface/trl)
    3. 🤗 Accelerate (huggingface/accelerate)
    4. 🤗 Datasets (huggingface/datasets)
    5. PyTorch (pytorch/pytorch)
    6. Stable Baselines3 (DLR-RM/stable-baselines3)
    7. Acme (deepmind/acme)
    8. TensorFlow (tensorflow/tensorflow)
    9. TF-Agents (tensorflow/agents)
    10. RLlib (ray-project/ray)

    AI recommended 10 alternatives but never named EvolvingLMMs-Lab/open-r1-multimodal. 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 EvolvingLMMs-Lab/open-r1-multimodal?
    pass
    AI did not name EvolvingLMMs-Lab/open-r1-multimodal — 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 EvolvingLMMs-Lab/open-r1-multimodal in production, what risks or prerequisites should they evaluate first?
    pass
    AI named EvolvingLMMs-Lab/open-r1-multimodal 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 EvolvingLMMs-Lab/open-r1-multimodal solve, and who is the primary audience?
    pass
    AI did not name EvolvingLMMs-Lab/open-r1-multimodal — 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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EvolvingLMMs-Lab/open-r1-multimodal — 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