RRepoGEO

REPOGEO REPORT · LITE

NVlabs/QeRL

Default branch main · commit 31e86dba · scanned 6/7/2026, 4:03:35 AM

GitHub: 506 stars · 51 forks

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 NVlabs/QeRL, 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
  • highreadme#1
    Add a disambiguation note for 'QeRL' in the README

    Why:

    COPY-PASTE FIX
    Add a sentence early in the README, e.g., 'QeRL stands for **Quantization-enhanced Reinforcement Learning**, and is distinct from 'Quantum-enhanced Reinforcement Learning'.'
  • highhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2510.11696
  • mediumtopics#3
    Add more specific topics to improve categorization

    Why:

    CURRENT
    llms, quantization, reasoning, reinforcement-learning
    COPY-PASTE FIX
    llms, quantization, reasoning, reinforcement-learning, rl-framework, llm-finetuning

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 NVlabs/QeRL
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
microsoft/DeepSpeed
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/DeepSpeed · recommended 1×
  2. NVIDIA/Megatron-LM · recommended 1×
  3. huggingface/accelerate · recommended 1×
  4. huggingface/peft · recommended 1×
  5. TimDettmers/bitsandbytes · recommended 1×
  • CATEGORY QUERY
    What are techniques for performing RL on 30B+ parameter LLMs with constrained hardware?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/DeepSpeed)
    2. Megatron-LM (NVIDIA/Megatron-LM)
    3. Accelerate (huggingface/accelerate)
    4. peft (huggingface/peft)
    5. bitsandbytes (TimDettmers/bitsandbytes)
    6. FlashAttention (Dao-AILab/flash-attention)
    7. Colossal-AI (hpcaitech/ColossalAI)

    AI recommended 7 alternatives but never named NVlabs/QeRL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for efficient reinforcement learning of large language models using quantization techniques?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Optimum
    2. 🤗 PEFT
    3. bitsandbytes
    4. QLoRA
    5. AWQ
    6. GPTQ
    7. AutoGPTQ
    8. ExLlamaV2
    9. NVIDIA TensorRT-LLM
    10. DeepSpeed
    11. PyTorch native quantization

    AI recommended 11 alternatives but never named NVlabs/QeRL. 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 NVlabs/QeRL?
    pass
    AI named NVlabs/QeRL explicitly

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

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

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

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NVlabs/QeRL — 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