RRepoGEO

REPOGEO REPORT · LITE

RLinf/RLinf

Default branch main · commit 364e522a · scanned 5/30/2026, 9:02:07 PM

GitHub: 3,582 stars · 491 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 RLinf/RLinf, 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
  • highreadme#1
    Strengthen the README's opening sentence to emphasize training and development

    Why:

    CURRENT
    RLinf is a flexible and scalable open-source RL infrastructure designed for Embodied and Agentic AI.
    COPY-PASTE FIX
    RLinf is a flexible and scalable open-source **training** infrastructure for **developing** Embodied and Agentic AI, providing a robust backbone for next-generation RL systems.
  • mediumtopics#2
    Add 'rl-training' to the repository topics

    Why:

    CURRENT
    agentic-ai, embodied-ai, reinforcement-learning, rl-infra, rlinf, vla-rl
    COPY-PASTE FIX
    agentic-ai, embodied-ai, reinforcement-learning, rl-infra, rlinf, vla-rl, rl-training
  • lowreadme#3
    Update 'What's NEW!' section dates to reflect current or past developments

    Why:

    CURRENT
    [2026/05] 🔥 RLinf supports RL training and SFT with Megatron-Bridge actor backend. Doc: Megatron-Bridge.
    COPY-PASTE FIX
    [2024/XX] 🔥 RLinf supports RL training and SFT with Megatron-Bridge actor backend. Doc: Megatron-Bridge. (Update dates to current or past, e.g., 2024/05 or 2023/12)

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 RLinf/RLinf
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 1×
  2. DLR-RM/stable-baselines3 · recommended 1×
  3. vwxyzjn/cleanrl · recommended 1×
  4. deepmind/open_spiel · recommended 1×
  5. thu-ml/tianshou · recommended 1×
  • CATEGORY QUERY
    Seeking scalable open-source reinforcement learning infrastructure for embodied and agentic AI development.
    you: not recommended
    AI recommended (in order):
    1. RLlib (ray-project/ray)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. CleanRL (vwxyzjn/cleanrl)
    4. OpenSpiel (deepmind/open_spiel)
    5. Tianshou (thu-ml/tianshou)

    AI recommended 5 alternatives but never named RLinf/RLinf. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks provide robust RL infrastructure for continuous generalization in agentic learning systems?
    you: not recommended
    AI recommended (in order):
    1. Ray RLLib
    2. Stable Baselines3 (SB3)
    3. Optuna
    4. Weights & Biases
    5. Acme
    6. Tianshou
    7. Dopamine
    8. OpenSpiel

    AI recommended 8 alternatives but never named RLinf/RLinf. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 RLinf/RLinf?
    pass
    AI named RLinf/RLinf explicitly

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

  • If a team adopts RLinf/RLinf in production, what risks or prerequisites should they evaluate first?
    pass
    AI named RLinf/RLinf 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 RLinf/RLinf solve, and who is the primary audience?
    pass
    AI named RLinf/RLinf 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 RLinf/RLinf. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/RLinf/RLinf.svg)](https://repogeo.com/en/r/RLinf/RLinf)
HTML
<a href="https://repogeo.com/en/r/RLinf/RLinf"><img src="https://repogeo.com/badge/RLinf/RLinf.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

RLinf/RLinf — 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