RRepoGEO

REPOGEO REPORT · LITE

lqtrung1998/mwp_ReFT

Default branch main · commit bdc844e0 · scanned 6/12/2026, 4:48:42 AM

GitHub: 554 stars · 65 forks

AI VISIBILITY SCORE
23 /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
2 / 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 lqtrung1998/mwp_ReFT, 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
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Official implementation of ReFT: Reasoning with REinforced Fine-Tuning for improving LLM performance on Math Word Problems by mitigating catastrophic forgetting.
  • hightopics#2
    Add relevant topics for discoverability

    Why:

    COPY-PASTE FIX
    llm, fine-tuning, reinforcement-learning, reasoning, math-word-problems, nlp, deep-learning, machine-learning, reft
  • mediumreadme#3
    Expand README introduction to clarify problem and solution

    Why:

    CURRENT
    # ReFT: Reasoning with REinforced Fine-Tuning
This repo contains source code and data to reproduce the results in the research paper ReFT: Reasoning with REinforced Fine-Tuning
    COPY-PASTE FIX
    # ReFT: Reasoning with REinforced Fine-Tuning
This repository provides the official implementation and data for "ReFT: Reasoning with REinforced Fine-Tuning," a novel method designed to significantly improve Large Language Models' (LLMs) ability to solve complex Math Word Problems. ReFT addresses challenges like catastrophic forgetting and enhances generalization by applying reinforced fine-tuning techniques. This repo contains source code and data to reproduce the results in the research paper.

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 lqtrung1998/mwp_ReFT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MATH
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MATH · recommended 1×
  2. GSM8K · recommended 1×
  3. AQuA · recommended 1×
  4. Wolfram Alpha · recommended 1×
  5. sympy/sympy · recommended 1×
  • CATEGORY QUERY
    How to improve large language model reasoning capabilities for mathematical problem-solving?
    you: not recommended
    AI recommended (in order):
    1. MATH
    2. GSM8K
    3. AQuA
    4. Wolfram Alpha
    5. SymPy (sympy/sympy)
    6. NumPy (numpy/numpy)
    7. SciPy (scipy/scipy)
    8. AlphaCode
    9. Minerva

    AI recommended 9 alternatives but never named lqtrung1998/mwp_ReFT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for fine-tuning language models using reinforcement learning for reasoning tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face TRL (huggingface/trl)
    2. Hugging Face Transformers (huggingface/transformers)
    3. DeepMind's Acme (deepmind/acme)
    4. OpenAI's Baselines (openai/baselines)
    5. PyTorch (pytorch/pytorch)
    6. JAX (google/jax)
    7. Ray RLlib (ray-project/ray)
    8. OpenAI Gym (openai/gym)
    9. Farama Gymnasium (Farama-Foundation/Gymnasium)
    10. OpenAI API
    11. Anthropic API
    12. LangChain (langchain-ai/langchain)
    13. LlamaIndex (run-llama/llama_index)

    AI recommended 13 alternatives but never named lqtrung1998/mwp_ReFT. 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 lqtrung1998/mwp_ReFT?
    pass
    AI named lqtrung1998/mwp_ReFT explicitly

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

  • If a team adopts lqtrung1998/mwp_ReFT in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lqtrung1998/mwp_ReFT 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 lqtrung1998/mwp_ReFT solve, and who is the primary audience?
    pass
    AI did not name lqtrung1998/mwp_ReFT — likely talking about a different project

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

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lqtrung1998/mwp_ReFT — 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