RRepoGEO

REPOGEO REPORT · LITE

Liuziyu77/Visual-RFT

Default branch main · commit 2ffad63b · scanned 5/18/2026, 7:32:43 AM

GitHub: 2,241 stars · 106 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 Liuziyu77/Visual-RFT, 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
    Reposition the README's core definition to the very top

    Why:

    CURRENT
    The current README starts with author list and 'Accepted By ICCV 2025!', followed by the descriptive sentence: '🌈We introduce **Visual Reinforcement Fine-tuning (Visual-RFT)**, the first comprehensive adaptation of **Deepseek-R1's RL strategy** to the **multimodal field**.'
    COPY-PASTE FIX
    Move the core definition to the very top of the README, before the author list and conference acceptance, e.g.: 'This repository introduces **Visual Reinforcement Fine-tuning (Visual-RFT)**, the first comprehensive adaptation of Deepseek-R1's RL strategy to the multimodal field, enabling advanced visual agentic capabilities for models like Qwen2-VL-2/7B. Accepted By ICCV 2025!'
  • mediumhomepage#2
    Add the paper link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2503.01785

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 Liuziyu77/Visual-RFT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stable Baselines3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Baselines3 · recommended 1×
  2. RLlib · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. DeepMind's Acme · recommended 1×
  5. PyTorch Lightning · recommended 1×
  • CATEGORY QUERY
    How to apply reinforcement learning for fine-tuning pre-trained visual models effectively?
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3
    2. RLlib
    3. Hugging Face Transformers
    4. DeepMind's Acme
    5. PyTorch Lightning

    AI recommended 5 alternatives but never named Liuziyu77/Visual-RFT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help with agentic reinforcement learning for visual task policy optimization?
    you: not recommended
    AI recommended (in order):
    1. RLlib (ray-project/ray)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. Acme (deepmind/acme)
    4. OpenSpiel (deepmind/open_spiel)
    5. TensorFlow Agents (tensorflow/agents)
    6. PyTorch Lightning (Lightning-AI/lightning)
    7. CleanRL (vwxyzjn/cleanrl)
    8. Dopamine (google/dopamine)

    AI recommended 8 alternatives but never named Liuziyu77/Visual-RFT. 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 Liuziyu77/Visual-RFT?
    pass
    AI named Liuziyu77/Visual-RFT explicitly

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

  • If a team adopts Liuziyu77/Visual-RFT in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Liuziyu77/Visual-RFT 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 Liuziyu77/Visual-RFT solve, and who is the primary audience?
    pass
    AI named Liuziyu77/Visual-RFT explicitly

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

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Liuziyu77/Visual-RFT — 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