RRepoGEO

REPOGEO REPORT · LITE

yongliang-wu/DFT

Default branch master · commit 3d46b252 · scanned 6/10/2026, 8:17:43 AM

GitHub: 574 stars · 24 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 yongliang-wu/DFT, 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
    Clarify the 'DFT' acronym in the README's introduction

    Why:

    CURRENT
    # *On the Generalization of SFT*: <br>A Reinforcement Learning Perspective with <br>Reward Rectification
    COPY-PASTE FIX
    This repository presents **DFT** (*On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification*), an implementation for our ICLR 2026 paper. It explores a reinforcement learning perspective with reward rectification to improve the generalization of supervised fine-tuning (SFT).
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text of a standard open-source license, such as the MIT License.

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 yongliang-wu/DFT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. huggingface/trl · recommended 2×
  3. pytorch/pytorch · recommended 2×
  4. deepmind/acme · recommended 1×
  5. openai/spinningup · recommended 1×
  • CATEGORY QUERY
    How to improve supervised fine-tuning generalization for large language models using reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. 🤗 Transformers (huggingface/transformers)
    2. TRL (huggingface/trl)
    3. DeepMind's Acme (deepmind/acme)
    4. OpenAI's Spinning Up (openai/spinningup)
    5. 🤗 Datasets (huggingface/datasets)
    6. NumPy (numpy/numpy)
    7. PyTorch (pytorch/pytorch)

    AI recommended 7 alternatives but never named yongliang-wu/DFT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking methods to enhance LLM training through novel reward rectification or shaping techniques.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. TRL (Transformer Reinforcement Learning) library (huggingface/trl)
    3. PyTorch (pytorch/pytorch)
    4. JAX (google/jax)
    5. Anthropic
    6. TensorFlow (tensorflow/tensorflow)
    7. Hugging Face Accelerate (huggingface/accelerate)
    8. Stable Baselines3 (DLR-RM/stable-baselines3)
    9. Ray RLlib (ray-project/ray)

    AI recommended 9 alternatives but never named yongliang-wu/DFT. 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 yongliang-wu/DFT?
    pass
    AI named yongliang-wu/DFT explicitly

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

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

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

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yongliang-wu/DFT — 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