RRepoGEO

REPOGEO REPORT · LITE

microsoft/rStar

Default branch main · commit ecbfb943 · scanned 5/29/2026, 3:42:15 PM

GitHub: 1,417 stars · 130 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 microsoft/rStar, 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
    Reposition the README's opening sentence to clarify project domain

    Why:

    CURRENT
    Repo for "rStar2-Agent: Agentic Reasoning Technical Report".
    COPY-PASTE FIX
    This repository provides the official code for **rStar2-Agent**, a framework enabling small Large Language Models (LLMs) to master agentic reasoning, math, and coding tasks, often leveraging reinforcement learning.
  • highabout#2
    Add a concise 'About' description for the repository

    Why:

    COPY-PASTE FIX
    Official code for rStar2-Agent: a framework for training small LLMs in agentic reasoning, math, and coding, often using reinforcement learning.

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 microsoft/rStar
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Code Alpaca
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Code Alpaca · recommended 1×
  2. MathInstruct · recommended 1×
  3. GSM8K · recommended 1×
  4. HumanEval · recommended 1×
  5. DeepMind's AlphaCode dataset · recommended 1×
  • CATEGORY QUERY
    How can I enhance small language model performance on complex math and coding problems?
    you: not recommended
    AI recommended (in order):
    1. Code Alpaca
    2. MathInstruct
    3. GSM8K
    4. HumanEval
    5. DeepMind's AlphaCode dataset
    6. Python Interpreter
    7. Wolfram Alpha API
    8. SymPy (sympy/sympy)
    9. LangChain (langchain-ai/langchain)
    10. LlamaIndex (run-llama/llama_index)
    11. Pinecone
    12. Weaviate (weaviate/weaviate)
    13. Chroma (chroma-core/chroma)
    14. Qdrant (qdrant/qdrant)
    15. Mixtral 8x7B

    AI recommended 15 alternatives but never named microsoft/rStar. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for training LLMs using reinforcement learning for reasoning tasks?
    you: not recommended
    AI recommended (in order):
    1. InstructGPT/ChatGPT
    2. Constitutional AI
    3. AlphaCode/AlphaTensor
    4. Self-Refine
    5. Toolformer
    6. PAL (Program-Aided Language Models)
    7. Codex/GitHub Copilot
    8. AlphaGeometry
    9. GATO
    10. Ray RLlib
    11. Stable Baselines3
    12. Tianshou
    13. Hugging Face Transformers

    AI recommended 13 alternatives but never named microsoft/rStar. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 microsoft/rStar?
    pass
    AI named microsoft/rStar explicitly

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

  • If a team adopts microsoft/rStar in production, what risks or prerequisites should they evaluate first?
    pass
    AI named microsoft/rStar 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 microsoft/rStar solve, and who is the primary audience?
    pass
    AI named microsoft/rStar 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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MARKDOWN (README)
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microsoft/rStar — 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