REPOGEO REPORT · LITE

lucidrains/PaLM-rlhf-pytorch

Default branch main · commit f736baeb · scanned 5/22/2026, 7:57:08 PM

GitHub: 7,864 stars · 679 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

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 lucidrains/PaLM-rlhf-pytorch, 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

Reposition README's opening to highlight specific implementation

Why:

CURRENT

## PaLM + RLHF - Pytorch (wip)
Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture.

COPY-PASTE FIX

## PaLM + RLHF - Pytorch (wip)
This repository offers a PyTorch implementation of Reinforcement Learning with Human Feedback (RLHF) specifically for the PaLM architecture, essentially providing the building blocks for a ChatGPT-like system using PaLM.

mediumtopics#2

Add more specific topics for better categorization

Why:

CURRENT

artificial-intelligence, attention-mechanisms, deep-learning, human-feedback, reinforcement-learning, transformers

COPY-PASTE FIX

artificial-intelligence, attention-mechanisms, deep-learning, human-feedback, reinforcement-learning, transformers, large-language-models, conversational-ai, palm-architecture, rlhf-pytorch

mediumhomepage#3
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
[Add a relevant URL, e.g., a project page, paper, or author's main page]
```

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 lucidrains/PaLM-rlhf-pytorch

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

huggingface/transformers · recommended 2×
huggingface/trl · recommended 2×
microsoft/DeepSpeed · recommended 2×
huggingface/peft · recommended 1×
OpenAI API · recommended 1×

CATEGORY QUERY
How to implement large language models with human feedback for conversational AI?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. PEFT (huggingface/peft)
3. TRL (huggingface/trl)
4. OpenAI API
5. DeepSpeed (microsoft/DeepSpeed)
6. Ray RLlib (ray-project/ray)
7. Pytorch Lightning (Lightning-AI/lightning)
8. Keras (keras-team/keras)
9. Argilla (argilla-io/argilla)
AI recommended 9 alternatives but never named lucidrains/PaLM-rlhf-pytorch. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What PyTorch libraries exist for training language models using reinforcement learning?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. TRL (huggingface/trl)
3. RLHF (Reinforcement Learning from Human Feedback) by OpenAI
4. DeepSpeed (microsoft/DeepSpeed)
5. PyTorch-RL (pytorch/rl)
6. CleanRL (vwxyzjn/cleanrl)
7. Stable Baselines3 (DLR-RM/stable-baselines3)
AI recommended 7 alternatives but never named lucidrains/PaLM-rlhf-pytorch. 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 lucidrains/PaLM-rlhf-pytorch?
pass
AI named lucidrains/PaLM-rlhf-pytorch explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts lucidrains/PaLM-rlhf-pytorch in production, what risks or prerequisites should they evaluate first?
pass
AI named lucidrains/PaLM-rlhf-pytorch 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 lucidrains/PaLM-rlhf-pytorch solve, and who is the primary audience?
pass
AI did not name lucidrains/PaLM-rlhf-pytorch — 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

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MARKDOWN (README)

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lucidrains/PaLM-rlhf-pytorch — 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