RRepoGEO

REPOGEO REPORT · LITE

xhyumiracle/Awesome-AgenticLLM-RL-Papers

Default branch main · commit 30061293 · scanned 5/15/2026, 12:44:09 PM

GitHub: 1,766 stars · 78 forks

AI VISIBILITY SCORE
17 /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
1 / 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 xhyumiracle/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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise description to the repository's About section

    Why:

    COPY-PASTE FIX
    A comprehensive, curated collection of research papers and resources on Agentic Reinforcement Learning for Large Language Models (LLMs), serving as the official repository for 'The Landscape of Agentic Reinforcement Learning for LLMs: A Survey'.
  • highreadme#2
    Refine the README's opening sentence to emphasize its role as a collection

    Why:

    CURRENT
    This is the Official repo for the survey paper: The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
    COPY-PASTE FIX
    This repository serves as the official, curated collection of research papers and resources for the survey: 'The Landscape of Agentic Reinforcement Learning for LLMs'.

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 xhyumiracle/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
Generative Agents: Interactive Simulacra of Human Behavior
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Generative Agents: Interactive Simulacra of Human Behavior · recommended 2×
  2. Voyager: An Open-Ended Embodied Agent with Large Language Models · recommended 1×
  3. Reflexion: Language Agents with Reinforcement Learning Fine-Tuning · recommended 1×
  4. RLHF-V: Towards Reliable Large Language Models via RLHF with Value Alignment · recommended 1×
  5. Language Models as Zero-Shot Reinforcement Learners · recommended 1×
  • CATEGORY QUERY
    What are the latest research papers on combining large language models with reinforcement learning agents?
    you: not recommended
    AI recommended (in order):
    1. Voyager: An Open-Ended Embodied Agent with Large Language Models
    2. Reflexion: Language Agents with Reinforcement Learning Fine-Tuning
    3. Generative Agents: Interactive Simulacra of Human Behavior
    4. RLHF-V: Towards Reliable Large Language Models via RLHF with Value Alignment
    5. Language Models as Zero-Shot Reinforcement Learners
    6. Large Language Models as General Pattern Machines
    7. Guiding Large Language Models with RL: A Survey

    AI recommended 7 alternatives but never named xhyumiracle/Awesome-AgenticLLM-RL-Papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive survey on agentic reinforcement learning algorithms for LLMs?
    you: not recommended
    AI recommended (in order):
    1. A Survey of Large Language Models in Reinforcement Learning
    2. Generative Agents: Interactive Simulacra of Human Behavior
    3. Foundation Models for Decision Making: Problems, Methods, and Opportunities
    4. Prompting Large Language Models for Autonomous Agent Systems: A Survey
    5. Reinforcement Learning from Human Feedback (RLHF): A Survey
    6. LLM-as-a-Judge: A Comprehensive Survey

    AI recommended 6 alternatives but never named xhyumiracle/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 xhyumiracle/Awesome-AgenticLLM-RL-Papers?
    pass
    AI did not name xhyumiracle/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 xhyumiracle/Awesome-AgenticLLM-RL-Papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named xhyumiracle/Awesome-AgenticLLM-RL-Papers 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 xhyumiracle/Awesome-AgenticLLM-RL-Papers solve, and who is the primary audience?
    pass
    AI did not name xhyumiracle/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

Drop this badge into the README of xhyumiracle/Awesome-AgenticLLM-RL-Papers. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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xhyumiracle/Awesome-AgenticLLM-RL-Papers — 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