RRepoGEO

REPOGEO REPORT · LITE

hkust-nlp/simpleRL-reason

Default branch v1 · commit cf1c7858 · scanned 6/28/2026, 1:07:42 PM

GitHub: 3,871 stars · 286 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 opening sentence to clarify its core purpose for LLMs

    Why:

    CURRENT
    This repo contains a simple reinforcement learning recipe to improve models' reasoning abilities. It is simple because only rule-based reward and GSM8K/Math datasets are used.
    COPY-PASTE FIX
    This repository provides a simple, effective reinforcement learning (RL) recipe specifically designed to enhance Large Language Models' (LLMs) reasoning abilities. It leverages rule-based rewards and limited datasets (like GSM8K/Math) to achieve significant accuracy gains, offering a lightweight alternative to complex RLHF frameworks for LLM fine-tuning.
  • mediumhomepage#2
    Add the arXiv paper link 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
huggingface/trl
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/trl · recommended 2×
  2. pytorch/pytorch · recommended 2×
  3. huggingface/transformers · recommended 2×
  4. microsoft/DeepSpeed · recommended 1×
  5. OpenAI's trl library · recommended 1×
  • CATEGORY QUERY
    Seeking a framework to fine-tune LLMs for better reasoning using rule-based rewards.
    you: not recommended
    AI recommended (in order):
    1. TRL (huggingface/trl)
    2. DeepSpeed-Chat (microsoft/DeepSpeed)
    3. OpenAI's trl library
    4. PyTorch (pytorch/pytorch)
    5. Ray RLlib (ray-project/ray)
    6. transformers (huggingface/transformers)
    7. accelerate (huggingface/accelerate)

    AI recommended 7 alternatives but never named hkust-nlp/simpleRL-reason. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Best practices for applying reinforcement learning to improve LLM reasoning on limited data?
    you: not recommended
    AI recommended (in order):
    1. ¡¡ Transformers (huggingface/transformers)
    2. TRL (huggingface/trl)
    3. OpenAI API
    4. Anthropic API
    5. LangChain (langchain-ai/langchain)
    6. LlamaIndex (run-llama/llama_index)
    7. PyTorch (pytorch/pytorch)
    8. TensorFlow (tensorflow/tensorflow)
    9. GPT-4
    10. Llama 3
    11. Stable Baselines3 (DLR-RM/stable-baselines3)

    AI recommended 11 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