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What is pal?

facebookresearch/pal — explained in plain English

Analysis updated 2026-07-18 · repo last pushed 2026-05-19

★ 40Jupyter NotebookAudience · researcherComplexity · 4/5MaintainedSetup · moderate

In one sentence

PAL is a research toolkit for reverse-engineering how large language models work internally, bundling code from published papers on reasoning, scaling, and optimization behavior.

Mindmap

mindmap
  root((PAL))
    What it does
      Studies LLM internals
      Reverse engineers behavior
      Collects research code
    Tech Stack
      Python
      Jupyter Notebook
    Research Areas
      Step by step reasoning
      Model scaling patterns
      Training optimization
      Internal visualization
    Audience
      AI researchers
      ML engineers
      Graduate students

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

What do people build with it?

USE CASE 1

Reproduce published NeurIPS, ICLR, or ICML experiments on how language models learn to reason step by step.

USE CASE 2

Study how large language model behavior changes as model size scales up.

USE CASE 3

Explore code investigating how neural networks optimize themselves during training.

USE CASE 4

Build new research on top of PAL's reusable core tools instead of starting from scratch.

What is it built with?

PythonJupyter Notebook

How does it compare?

facebookresearch/palvt-vl-lab/video-data-augcohlem/nanoclaude
Stars403331
LanguageJupyter NotebookJupyter NotebookJupyter Notebook
Last pushed2026-05-192021-10-26—
MaintenanceMaintainedDormant—
Setup difficultymoderatehardeasy
Complexity4/55/52/5
Audienceresearcherresearcherdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 1h+

Requires cloning the repo, installing as a Python package, and familiarity with the referenced research papers to interpret results.

Copy-paste prompts

Prompt 1
Show me how to clone and install PAL as a Python package, then run one of the example research scripts.
Prompt 2
Explain how PAL's research on step-by-step reasoning in language models works and what the code in that folder does.
Prompt 3
Walk me through PAL's folder structure so I can tell the difference between the reusable core tools and individual research projects.
Prompt 4
What does PAL's research on how neural networks optimize themselves during training show, based on the code in this repo?

Frequently asked questions

What is pal?

PAL is a research toolkit for reverse-engineering how large language models work internally, bundling code from published papers on reasoning, scaling, and optimization behavior.

What language is pal written in?

Mainly Jupyter Notebook. The stack also includes Python, Jupyter Notebook.

Is pal actively maintained?

Maintained — commit in last 6 months (last push 2026-05-19).

How hard is pal to set up?

Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.

Who is pal for?

Mainly researcher.

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