RRepoGEO

REPOGEO REPORT · LITE

HHHHHejia/Awesome-AgenticLLM-RL-Papers

Default branch main · commit 9b577322 · scanned 6/20/2026, 1:53:00 PM

GitHub: 1,819 stars · 80 forks

AI VISIBILITY SCORE
10 /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
0 / 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 HHHHHejia/Awesome-AgenticLLM-RL-Papers, 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 repository for 'The Landscape of Agentic Reinforcement Learning for LLMs: A Survey', providing a curated list of papers and resources on agentic RL algorithms for large language models.
  • hightopics#2
    Add relevant repository topics

    Why:

    COPY-PASTE FIX
    agentic-llm, reinforcement-learning, llm-agents, survey-paper, machine-learning, artificial-intelligence, research-papers, awesome-list
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text for the Creative Commons Attribution 4.0 International License (CC-BY-4.0). This license is suitable for the content of a survey paper and curated list.

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 HHHHHejia/Awesome-AgenticLLM-RL-Papers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face TRL
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face TRL · recommended 3×
  2. OpenAI API · recommended 1×
  3. Anthropic API · recommended 1×
  4. Stable Baselines3 · recommended 1×
  5. Ray RLlib · recommended 1×
  • CATEGORY QUERY
    How can I apply reinforcement learning to enhance the capabilities of large language model agents?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face TRL
    2. Hugging Face TRL
    3. OpenAI API
    4. Anthropic API
    5. Stable Baselines3
    6. Ray RLlib
    7. DeepMind Acme
    8. Hugging Face TRL

    AI recommended 8 alternatives but never named HHHHHejia/Awesome-AgenticLLM-RL-Papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a survey of algorithms for agentic reinforcement learning with LLMs?
    you: not recommended
    AI recommended (in order):
    1. Foundation Models for Decision Making: Problems, Methods, and Opportunities
    2. Language Models are Zero-Shot Reinforcement Learners
    3. Generative Agents: Interactive Simulacra of Human Behavior
    4. Reinforcement Learning from Human Feedback (RLHF)
    5. ChatGPT
    6. Claude
    7. Prompt Engineering for Large Language Models: A Survey
    8. Awesome-LLM-Agents

    AI recommended 8 alternatives but never named HHHHHejia/Awesome-AgenticLLM-RL-Papers. 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 HHHHHejia/Awesome-AgenticLLM-RL-Papers?
    pass
    AI did not name HHHHHejia/Awesome-AgenticLLM-RL-Papers — 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?

  • If a team adopts HHHHHejia/Awesome-AgenticLLM-RL-Papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name HHHHHejia/Awesome-AgenticLLM-RL-Papers — 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?

  • In one sentence, what problem does the repo HHHHHejia/Awesome-AgenticLLM-RL-Papers solve, and who is the primary audience?
    pass
    AI did not name HHHHHejia/Awesome-AgenticLLM-RL-Papers — 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?

Embed your GEO score

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HHHHHejia/Awesome-AgenticLLM-RL-Papers — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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