RRepoGEO

REPOGEO REPORT · LITE

CarperAI/trlx

Default branch main · commit 3340c2f3 · scanned 6/19/2026, 7:41:52 PM

GitHub: 4,749 stars · 484 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 CarperAI/trlx, 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
    Move 'CHEESE' project mention to a 'Related Projects' section

    Why:

    CURRENT
    🧀 **CHEESE** Collect human annotations for your RL application with our human-in-the-loop data collection library.
    COPY-PASTE FIX
    Create a new top-level section, e.g., '## Related Projects' and move the 'CHEESE' description there. Ensure the main README focuses solely on trlX as a training framework.
  • hightopics#2
    Expand repository topics for better category matching

    Why:

    CURRENT
    machine-learning, pytorch, reinforcement-learning
    COPY-PASTE FIX
    machine-learning, pytorch, reinforcement-learning, distributed-training, large-language-models, llm-fine-tuning, rlhf, deepspeed, accelerate, nemo
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/CarperAI/trlx

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 CarperAI/trlx
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. DeepSpeed · recommended 2×
  3. Argilla · recommended 1×
  4. Prodigy · recommended 1×
  5. Surge AI · recommended 1×
  • CATEGORY QUERY
    How to apply reinforcement learning from human feedback to fine-tune large language models?
    you: not recommended
    AI recommended (in order):
    1. Argilla
    2. Prodigy
    3. Surge AI
    4. Scale AI
    5. Hugging Face Transformers
    6. PyTorch
    7. TensorFlow
    8. DeepSpeed
    9. FSDP
    10. Hugging Face TRL
    11. OpenAI Baselines
    12. Stable Baselines3
    13. DeepSpeed-Chat
    14. GPT-4
    15. Claude 3

    AI recommended 15 alternatives but never named CarperAI/trlx. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks support distributed PPO or ILQL for scaling large language model fine-tuning?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Accelerate
    3. DeepSpeed
    4. PyTorch FSDP
    5. RLHF (Reinforcement Learning from Human Feedback) by Hugging Face
    6. TRL (Transformer Reinforcement Learning) by Hugging Face
    7. Ray RLlib
    8. Ray
    9. Colossal-AI
    10. Megatron-LM (NVIDIA)

    AI recommended 10 alternatives but never named CarperAI/trlx. 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 CarperAI/trlx?
    pass
    AI named CarperAI/trlx explicitly

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

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

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

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CarperAI/trlx — 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