RRepoGEO

REPOGEO REPORT · LITE

openai/large-scale-curiosity

Default branch master · commit e0a69867 · scanned 6/3/2026, 1:12:56 AM

GitHub: 829 stars · 184 forks

AI VISIBILITY SCORE
28 /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
2 / 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 openai/large-scale-curiosity, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root, specifying the terms under which the code can be used.
  • hightopics#2
    Add specific topics for intrinsic motivation and reinforcement learning

    Why:

    CURRENT
    ["paper"]
    COPY-PASTE FIX
    ["reinforcement-learning", "intrinsic-motivation", "curiosity-driven-learning", "deep-learning", "tensorflow", "prediction-error", "exploration", "machine-learning"]
  • mediumreadme#3
    Clarify the project's core method and category in the README's opening

    Why:

    CURRENT
    Status: Archive (code is provided as-is, no updates expected)

## Large-Scale Study of Curiosity-Driven Learning ##
    COPY-PASTE FIX
    Status: Archive (code is provided as-is, no updates expected)

## Large-Scale Study of Curiosity-Driven Learning: A TensorFlow Implementation of Intrinsic Motivation ##

This repository provides the TensorFlow implementation for our paper on large-scale curiosity-driven learning, a method for intrinsic motivation in reinforcement learning using prediction error as a reward signal.

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 openai/large-scale-curiosity
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Random Network Distillation (RND)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Random Network Distillation (RND) · recommended 1×
  2. Novelty Search (NS) · recommended 1×
  3. Intrinsic Curiosity Module (ICM) · recommended 1×
  4. Never Give Up (NGU) · recommended 1×
  5. Diversity Is All You Need (DIAYN) · recommended 1×
  • CATEGORY QUERY
    How to implement intrinsic reward functions for reinforcement learning agents in novel environments?
    you: not recommended
    AI recommended (in order):
    1. Random Network Distillation (RND)
    2. Novelty Search (NS)
    3. Intrinsic Curiosity Module (ICM)
    4. Never Give Up (NGU)
    5. Diversity Is All You Need (DIAYN)
    6. Hindsight Experience Replay (HER)

    AI recommended 6 alternatives but never named openai/large-scale-curiosity. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for intrinsic motivation in RL using prediction error with TensorFlow?
    you: not recommended
    AI recommended (in order):
    1. TF-Agents
    2. TensorFlow Probability

    AI recommended 2 alternatives but never named openai/large-scale-curiosity. 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 openai/large-scale-curiosity?
    pass
    AI named openai/large-scale-curiosity explicitly

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

  • If a team adopts openai/large-scale-curiosity in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/large-scale-curiosity 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 openai/large-scale-curiosity solve, and who is the primary audience?
    pass
    AI did not name openai/large-scale-curiosity — 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?

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openai/large-scale-curiosity — 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