RRepoGEO

REPOGEO REPORT · LITE

tinkoff-ai/CORL

Default branch main · commit 6afec904 · scanned 5/11/2026, 12:47:07 PM

GitHub: 1,355 stars · 165 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 tinkoff-ai/CORL, 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
    Reposition README's opening paragraph to emphasize toolkit and benchmarking

    Why:

    CURRENT
    🧵 CORL is an Offline Reinforcement Learning library that provides high-quality and easy-to-follow single-file implementations of SOTA ORL algorithms. Each implementation is backed by a research-friendly codebase, allowing you to run or tune thousands of experiments. Heavily inspired by cleanrl for online RL, check them out too!<br/>
    COPY-PASTE FIX
    CORL is a research-friendly toolkit for Offline Reinforcement Learning (ORL), offering high-quality, single-file implementations of state-of-the-art ORL algorithms. Designed for robust benchmarking and rapid experimentation, CORL allows researchers to easily run and tune thousands of experiments, drawing inspiration from CleanRL for online RL.
  • hightopics#2
    Expand repository topics with specific keywords

    Why:

    CURRENT
    d4rl, gym, offline-reinforcement-learning, reinforcement-learning
    COPY-PASTE FIX
    d4rl, gym, offline-reinforcement-learning, reinforcement-learning, deep-reinforcement-learning, benchmarking, sota-algorithms, machine-learning-research
  • mediumreadme#3
    Add a 'Why CORL?' section highlighting unique differentiators

    Why:

    COPY-PASTE FIX
    ### Why CORL?
Unlike many comprehensive frameworks, CORL prioritizes:
*   **Single-File Simplicity:** Each algorithm is implemented in a single, easy-to-understand file, reducing complexity and accelerating research.
*   **Research-Friendly Codebase:** Designed for rapid iteration and benchmarking, allowing researchers to quickly adapt and extend SOTA algorithms.

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 tinkoff-ai/CORL
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
d3rlpy
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. d3rlpy · recommended 2×
  2. RL Unplugged · recommended 2×
  3. Acme · recommended 2×
  4. Stable Baselines3 · recommended 1×
  5. RLlib · recommended 1×
  • CATEGORY QUERY
    What are good libraries for implementing state-of-the-art offline reinforcement learning algorithms?
    you: not recommended
    AI recommended (in order):
    1. d3rlpy
    2. RL Unplugged
    3. Stable Baselines3
    4. Acme
    5. RLlib
    6. CleanRL

    AI recommended 6 alternatives but never named tinkoff-ai/CORL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a research-friendly toolkit for benchmarking various offline reinforcement learning algorithms.
    you: not recommended
    AI recommended (in order):
    1. d3rlpy
    2. RL Unplugged
    3. Stable Baselines3 (SB3)
    4. Open X-Embodiment (OXE) Datasets and Ecosystem
    5. RLHive
    6. Acme

    AI recommended 6 alternatives but never named tinkoff-ai/CORL. 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 tinkoff-ai/CORL?
    pass
    AI named tinkoff-ai/CORL explicitly

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

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

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

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tinkoff-ai/CORL — 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