RRepoGEO

REPOGEO REPORT · LITE

Unakar/Logic-RL

Default branch main · commit 9d2c4575 · scanned 5/23/2026, 1:18:14 PM

GitHub: 2,450 stars · 164 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 Unakar/Logic-RL, 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
  • highabout#1
    Broaden the repository description

    Why:

    CURRENT
    Reproduce R1 Zero on Logic Puzzle
    COPY-PASTE FIX
    A framework for unleashing LLM reasoning with rule-based reinforcement learning, focusing on complex logical puzzles.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm, reinforcement-learning, logical-reasoning, nlp, ai-research, rule-based-ai
  • mediumhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2502.14768

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 Unakar/Logic-RL
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Code Interpreter
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Code Interpreter · recommended 1×
  2. Google's PAL · recommended 1×
  3. langchain-ai/langchain · recommended 1×
  4. run-llama/llama_index · recommended 1×
  5. Google's Minerva · recommended 1×
  • CATEGORY QUERY
    How to enhance large language model reasoning capabilities for complex logical puzzles?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Code Interpreter
    2. Google's PAL
    3. LangChain (langchain-ai/langchain)
    4. LlamaIndex (run-llama/llama_index)
    5. Google's Minerva
    6. DeepMind's AlphaCode

    AI recommended 6 alternatives but never named Unakar/Logic-RL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks exist for applying reinforcement learning to steer LLM reasoning trajectories?
    you: not recommended
    AI recommended (in order):
    1. RLHF (Reinforcement Learning from Human Feedback)
    2. TRL (Transformer Reinforcement Learning) (huggingface/trl)
    3. DeepSpeed-Chat (microsoft/DeepSpeed-Chat)
    4. ColossalAI
    5. TacticAI
    6. Farm-RL

    AI recommended 6 alternatives but never named Unakar/Logic-RL. 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 Unakar/Logic-RL?
    pass
    AI named Unakar/Logic-RL explicitly

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

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

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

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Unakar/Logic-RL — 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