RRepoGEO

REPOGEO REPORT · LITE

sail-sg/oat

Default branch main · commit 86970662 · scanned 6/1/2026, 7:22:07 PM

GitHub: 658 stars · 63 forks

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 sail-sg/oat, 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
    Add a prominent, unambiguous H1 to the README clarifying OAT's purpose

    Why:

    CURRENT
    The README's first substantive text after badges/links is in an 'Introduction' section: 'Oat 🌾 is a simple yet efficient framework for running **online** LLM alignment algorithms.'
    COPY-PASTE FIX
    # OAT 🌾: A Research-Friendly Framework for Online LLM Alignment
OAT is designed for efficient online reinforcement learning and preference learning to continuously improve large language models.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2411.01493
  • mediumreadme#3
    Emphasize 'research-friendly' aspect in README introduction

    Why:

    CURRENT
    Oat 🌾 is a simple yet efficient framework for running **online** LLM alignment algorithms.
    COPY-PASTE FIX
    Oat 🌾 is a simple yet efficient, **research-friendly** framework for running **online** LLM alignment 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 sail-sg/oat
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/trl
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/trl · recommended 1×
  2. microsoft/DeepSpeed-Chat · recommended 1×
  3. ray-project/ray · recommended 1×
  4. DLR-RM/stable-baselines3 · recommended 1×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    Framework for continuously improving large language models through online reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. trl (huggingface/trl)
    2. DeepSpeed-Chat (microsoft/DeepSpeed-Chat)
    3. Ray RLlib (ray-project/ray)
    4. Stable Baselines3 (DLR-RM/stable-baselines3)
    5. PyTorch (pytorch/pytorch)
    6. TensorFlow (tensorflow/tensorflow)

    AI recommended 6 alternatives but never named sail-sg/oat. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement distributed online reinforcement learning for LLM alignment research efficiently?
    you: not recommended
    AI recommended (in order):
    1. Ray RLlib
    2. DeepMind Acme
    3. JAX
    4. Flax
    5. Orbax
    6. T5X
    7. Hugging Face Accelerate
    8. PyTorch
    9. FSDP
    10. RL-X

    AI recommended 10 alternatives but never named sail-sg/oat. 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 sail-sg/oat?
    pass
    AI named sail-sg/oat explicitly

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

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

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

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sail-sg/oat — 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