RRepoGEO

REPOGEO REPORT · LITE

openai/evolution-strategies-starter

Default branch master · commit 951f1998 · scanned 5/21/2026, 10:03:40 PM

GitHub: 1,630 stars · 280 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
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 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 openai/evolution-strategies-starter, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    CURRENT
    paper
    COPY-PASTE FIX
    evolution-strategies, reinforcement-learning-alternative, distributed-ml, research-code, aws-ec2
  • highreadme#2
    Reposition the README H1 and opening paragraph to clarify purpose

    Why:

    CURRENT
    # Distributed evolution

This is a distributed implementation of the algorithm described in Evolution Strategies as a Scalable Alternative to Reinforcement Learning (Tim Salimans, Jonathan Ho, Xi Chen, Ilya Sutskever).
    COPY-PASTE FIX
    # Evolution Strategies: Reference Implementation for Scalable RL Alternative

This repository provides the archived, distributed implementation of the Evolution Strategies algorithm as described in the paper 'Evolution Strategies as a Scalable Alternative to Reinforcement Learning'.
  • mediumabout#3
    Clarify the 'About' description to specify its nature as a reference implementation

    Why:

    CURRENT
    Code for the paper "Evolution Strategies as a Scalable Alternative to Reinforcement Learning"
    COPY-PASTE FIX
    Archived reference implementation of Evolution Strategies for scalable reinforcement learning, as detailed in the paper 'Evolution Strategies as a Scalable Alternative to Reinforcement Learning'.

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/evolution-strategies-starter
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 3×
  2. tensorflow/tensorflow · recommended 1×
  3. pytorch/pytorch · recommended 1×
  4. deepmind/acme · recommended 1×
  5. openai/baselines · recommended 1×
  • CATEGORY QUERY
    How to scale reinforcement learning tasks efficiently using alternative optimization methods?
    you: not recommended
    AI recommended (in order):
    1. Ray RLLib (ray-project/ray)
    2. Ray (ray-project/ray)
    3. TensorFlow (tensorflow/tensorflow)
    4. PyTorch (pytorch/pytorch)
    5. Acme (deepmind/acme)
    6. OpenAI Baselines (openai/baselines)
    7. MPI (Message Passing Interface)
    8. Horovod (horovod/horovod)
    9. JAX (google/jax)
    10. Ray Tune (ray-project/ray)
    11. Dopamine (google/dopamine)
    12. Seed RL (google-research/seed_rl)

    AI recommended 12 alternatives but never named openai/evolution-strategies-starter. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a distributed framework to implement evolutionary algorithms on cloud infrastructure.
    you: not recommended
    AI recommended (in order):
    1. Ray
    2. Apache Spark
    3. Dask
    4. Celery
    5. AWS Batch
    6. Google Cloud Batch
    7. Azure Batch
    8. Kubernetes
    9. Kubeflow

    AI recommended 9 alternatives but never named openai/evolution-strategies-starter. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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/evolution-strategies-starter?
    pass
    AI did not name openai/evolution-strategies-starter — 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 openai/evolution-strategies-starter in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/evolution-strategies-starter 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/evolution-strategies-starter solve, and who is the primary audience?
    pass
    AI did not name openai/evolution-strategies-starter — 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/evolution-strategies-starter — 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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