RRepoGEO

REPOGEO REPORT · LITE

yifan123/flow_grpo

Default branch main · commit 879042cf · scanned 6/28/2026, 4:53:13 AM

GitHub: 2,369 stars · 165 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 yifan123/flow_grpo, 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
    Add a concise, category-defining sentence to the README's opening

    Why:

    CURRENT
    <h1 align="center"> Flow-GRPO:<br>Training Flow Matching Models via Online RL </h1>
    COPY-PASTE FIX
    <h1 align="center"> Flow-GRPO:<br>Training Flow Matching Models via Online RL </h1>

This repository provides the official implementation for Flow-GRPO, a novel approach to training flow matching generative models using online reinforcement learning techniques.
  • mediumabout#2
    Expand the 'About' description with more keywords

    Why:

    CURRENT
    [NeurIPS 2025] An official implementation of Flow-GRPO: Training Flow Matching Models via Online RL
    COPY-PASTE FIX
    [NeurIPS 2025] Official implementation of Flow-GRPO: a method for training flow matching generative models efficiently via online reinforcement learning (RL) for improved policy optimization.

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 yifan123/flow_grpo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ray RLib
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Ray RLib · recommended 1×
  2. Optuna · recommended 1×
  3. Acme · recommended 1×
  4. Dopamine · recommended 1×
  5. huggingface/diffusers · recommended 1×
  • CATEGORY QUERY
    How can I improve flow matching model training efficiency using reinforcement learning techniques?
    you: not recommended
    AI recommended (in order):
    1. Ray RLib
    2. Optuna
    3. Acme
    4. Dopamine

    AI recommended 4 alternatives but never named yifan123/flow_grpo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for training generative models with flow matching and online RL?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Diffusers (huggingface/diffusers)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. PyTorch (pytorch/pytorch)
    4. TensorFlow (tensorflow/tensorflow)
    5. torchdiffeq (rtqichen/torchdiffeq)
    6. Pyro (pyro-ppl/pyro)
    7. RLlib (ray-project/ray)
    8. CleanRL (vwxyzjn/cleanrl)
    9. JAX (google/jax)
    10. Flax (google/flax)

    AI recommended 10 alternatives but never named yifan123/flow_grpo. 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 yifan123/flow_grpo?
    pass
    AI named yifan123/flow_grpo explicitly

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

  • If a team adopts yifan123/flow_grpo in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yifan123/flow_grpo 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 yifan123/flow_grpo solve, and who is the primary audience?
    pass
    AI named yifan123/flow_grpo explicitly

    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 yifan123/flow_grpo. 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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yifan123/flow_grpo — 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