RRepoGEO

REPOGEO REPORT · LITE

hkust-nlp/simpleRL-reason

Default branch v1 · commit cf1c7858 · scanned 5/17/2026, 10:53:31 AM

GitHub: 3,857 stars · 290 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 hkust-nlp/simpleRL-reason, 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 H1 and opening sentence for LLM reasoning

    Why:

    CURRENT
    # Simple Reinforcement Learning for Reasoning

This repo contains a simple reinforcement learning recipe to improve models' reasoning abilities.
    COPY-PASTE FIX
    # Simple Reinforcement Learning for LLM Reasoning

This repository provides a simple reinforcement learning recipe specifically designed to improve the mathematical and logical reasoning abilities of large language models (LLMs).
  • mediumhomepage#2
    Add the arXiv paper URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2503.18892

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 hkust-nlp/simpleRL-reason
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI's Code Interpreter
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI's Code Interpreter · recommended 1×
  2. Google's AlphaCode · recommended 1×
  3. sympy/sympy · recommended 1×
  4. Wolfram Alpha API · recommended 1×
  5. GSM8K · recommended 1×
  • CATEGORY QUERY
    How can I improve large language models' mathematical reasoning capabilities with limited training data?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's Code Interpreter
    2. Google's AlphaCode
    3. SymPy (sympy/sympy)
    4. Wolfram Alpha API
    5. GSM8K
    6. MATH dataset
    7. AQuA-RAT
    8. LangChain (langchain-ai/langchain)
    9. LlamaIndex (run-llama/llama_index)
    10. Hugging Face PEFT (huggingface/peft)
    11. GPT-4
    12. Claude 3 Opus

    AI recommended 12 alternatives but never named hkust-nlp/simpleRL-reason. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective reinforcement learning techniques for enhancing a model's problem-solving and logical reasoning?
    you: not recommended
    AI recommended (in order):
    1. Proximal Policy Optimization (PPO)
    2. Soft Actor-Critic (SAC)
    3. AlphaZero
    4. MuZero
    5. Recurrent Neural Networks (RNNs)
    6. LSTMs
    7. GRUs
    8. Transformers
    9. Curriculum Learning
    10. Hindsight Experience Replay (HER)
    11. Reward Shaping
    12. Intrinsic Motivation
    13. Exploration via Disagreement
    14. Random Network Distillation (RND)
    15. Graph Neural Networks (GNNs)

    AI recommended 15 alternatives but never named hkust-nlp/simpleRL-reason. 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 hkust-nlp/simpleRL-reason?
    pass
    AI named hkust-nlp/simpleRL-reason explicitly

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

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

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

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hkust-nlp/simpleRL-reason — 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