RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/swe-rl

Default branch main · commit 5aa10d67 · scanned 6/16/2026, 1:48:11 AM

GitHub: 701 stars · 60 forks

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 facebookresearch/swe-rl, 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
  • mediumreadme#1
    Clarify the repository's license in the README

    Why:

    COPY-PASTE FIX
    Add a section or line in the README, perhaps under 'About' or a new 'License' section, stating: 'This project is licensed under [Specify License(s) here, e.g., a custom license, or a combination of licenses like Apache-2.0 and MIT]. Please see the LICENSE file for full details.'
  • mediumreadme#2
    Emphasize the 'benchmark and interactive environment' aspect in the README's introduction

    Why:

    CURRENT
    Official codebase for our paper: **SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution** (link).
    COPY-PASTE FIX
    Official codebase for our paper: **SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution** (link). SWE-RL provides a novel benchmark and interactive environment for applying RL to LLM reasoning in real-world software engineering tasks.

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 facebookresearch/swe-rl
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. OpenAI GPT-4 · recommended 1×
  3. Anthropic Claude 3 Opus · recommended 1×
  4. Google Gemini 1.5 Pro · recommended 1×
  5. OpenAI's Fine-tuning API · recommended 1×
  • CATEGORY QUERY
    How to improve large language model reasoning for complex software engineering tasks?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. Anthropic Claude 3 Opus
    3. Google Gemini 1.5 Pro
    4. OpenAI's Fine-tuning API
    5. Hugging Face Transformers
    6. Llama 2
    7. Mistral
    8. LangChain
    9. LlamaIndex
    10. Code Llama
    11. StarCoder2

    AI recommended 11 alternatives but never named facebookresearch/swe-rl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks use reinforcement learning to enhance LLM performance in software evolution?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. TRL
    3. PEFT
    4. Ray RLlib
    5. Stable Baselines3
    6. DeepMind's Acme
    7. OpenAI Gym
    8. Farama Gymnasium

    AI recommended 8 alternatives but never named facebookresearch/swe-rl. 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 facebookresearch/swe-rl?
    pass
    AI named facebookresearch/swe-rl explicitly

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

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

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

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facebookresearch/swe-rl — 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