RRepoGEO

REPOGEO REPORT · LITE

radixark/miles

Default branch main · commit d94ee8cb · scanned 5/13/2026, 1:58:03 PM

GitHub: 1,326 stars · 197 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 radixark/miles, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    reinforcement-learning, llm, vlm, large-language-models, vision-language-models, enterprise-ai, machine-learning, deep-learning, post-training, quantization, multi-agent-systems
  • mediumreadme#2
    Add a concise, disambiguating introductory sentence to the README

    Why:

    CURRENT
    The README currently starts with a `<div align="center">` block containing the H1.
    COPY-PASTE FIX
    Miles is an enterprise-grade reinforcement learning framework for LLM and VLM post-training, forked from and co-evolving with `slime`.
  • lowhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/radixark/miles

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 radixark/miles
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
microsoft/DeepSpeed
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/DeepSpeed · recommended 3×
  2. OpenAI API · recommended 1×
  3. Azure OpenAI Service · recommended 1×
  4. huggingface/trl · recommended 1×
  5. ray-project/ray · recommended 1×
  • CATEGORY QUERY
    How to apply reinforcement learning effectively for large language model post-training in an enterprise setting?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Azure OpenAI Service
    3. Hugging Face TRL (huggingface/trl)
    4. Ray RLlib (ray-project/ray)
    5. DeepMind's Acme (deepmind/acme)
    6. Microsoft DeepSpeed-MII (microsoft/DeepSpeed)
    7. DeepSpeed-RL (microsoft/DeepSpeed)
    8. Google Cloud Vertex AI
    9. Amazon SageMaker

    AI recommended 9 alternatives but never named radixark/miles. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Framework for efficient low-precision reinforcement learning with large vision and language models?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/DeepSpeed)
    2. PyTorch Lightning (Lightning-AI/lightning)
    3. Hugging Face Accelerate (huggingface/accelerate)
    4. TensorFlow (tensorflow/tensorflow)
    5. ONNX Runtime (microsoft/onnxruntime)
    6. NVIDIA Apex (NVIDIA/apex)

    AI recommended 6 alternatives but never named radixark/miles. 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 radixark/miles?
    pass
    AI named radixark/miles explicitly

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

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

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

Embed your GEO score

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radixark/miles — 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