REPOGEO REPORT · LITE

walkinglabs/hands-on-modern-rl

Default branch main · commit 4965808a · scanned 5/8/2026, 6:43:11 AM

GitHub: 1,320 stars · 65 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)

4 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 walkinglabs/hands-on-modern-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

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

OVERALL DIRECTION

highreadme#1

Add a clear English introductory sentence to the README

Why:

COPY-PASTE FIX

Insert the following sentence directly after the main H1 title in the README: 'This is an open-source, hands-on curriculum bridging the gap from basic RL concepts to LLM alignment, RLVR, and advanced Agentic systems.'

mediumtopics#2
Correct typo in 'reinforcemen' topic
Why:
CURRENT
```
reinforcemen
```
COPY-PASTE FIX
```
reinforcement
```

mediumreadme#3

Clarify the repository's license in the README

Why:

COPY-PASTE FIX

Add a '## License' section to the README stating: 'This project is licensed under the terms outlined in the [LICENSE file](LICENSE). Please refer to the file for specific details.'

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 walkinglabs/hands-on-modern-rl

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Deep Reinforcement Learning (DRL) Bootcamp by Sergey Levine (UC Berkeley)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Deep Reinforcement Learning (DRL) Bootcamp by Sergey Levine (UC Berkeley) · recommended 1×
spinningup.openai.com · recommended 1×
Hugging Face Reinforcement Learning Course · recommended 1×
Reinforcement Learning: An Introduction by Sutton and Barto · recommended 1×
Alignment Research Center (ARC) Resources · recommended 1×

CATEGORY QUERY
Looking for a practical curriculum to learn modern reinforcement learning and LLM alignment techniques.
you: not recommended
AI recommended (in order):
1. Deep Reinforcement Learning (DRL) Bootcamp by Sergey Levine (UC Berkeley)
2. spinningup.openai.com
3. Hugging Face Reinforcement Learning Course
4. Reinforcement Learning: An Introduction by Sutton and Barto
5. Alignment Research Center (ARC) Resources
6. Anthropic's Constitutional AI and RL from AI Feedback (RLAIF) Papers/Blog Posts
7. DeepMind's Learning from Human Feedback (RLHF) Papers
AI recommended 7 alternatives but never named walkinglabs/hands-on-modern-rl. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best practical guides for fine-tuning large language models with RLHF and DPO?
you: not recommended
AI recommended (in order):
1. Hugging Face
2. PEFT
3. TRL
4. OpenAI
5. InstructGPT
6. ChatGPT
7. AlpacaFarm
8. Llama-2-finetune
9. Llama 2
10. makemore
11. DeepMind
12. Sparrow
13. Chinchilla
AI recommended 13 alternatives but never named walkinglabs/hands-on-modern-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
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 walkinglabs/hands-on-modern-rl?
pass
AI did not name walkinglabs/hands-on-modern-rl — 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 walkinglabs/hands-on-modern-rl in production, what risks or prerequisites should they evaluate first?
pass
AI named walkinglabs/hands-on-modern-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 walkinglabs/hands-on-modern-rl solve, and who is the primary audience?
pass
AI did not name walkinglabs/hands-on-modern-rl — 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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MARKDOWN (README)

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walkinglabs/hands-on-modern-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