This page mirrors
CONSTITUTION.md at the repository root, which is the canonical copy; a test keeps the two identical. The three agents that apply it on demand live in .claude/agents/: style-guide (the SDK surface against the style guide and the book), docs-designer (these docs and the website, with a designer’s page contract), user-sim (a researcher running a recipe on their own keys and compute, filing what got in the way).whileai is, what we believe, and how that shows up in the code.
Read it before you add a public name, write a page, or run a recipe. The
routines that maintain this repo read it too.
What we are
whileai is a scientific post-training library for language models: SFT
and RL, on open models, with the measurement that says whether training
helped. Simulate, grade, measure with intervals, select, train, prove on a
held-out set, serve, and feed the new traces back in. Build self-improving
systems.
It is for AI researchers, ML engineers and applied-AI developers, and the
goal is that it sits in every applied-AI and research department the way
PyTorch does. The platform (whileai.platform) is a separate, optional
service for hosted training and serving. The library needs no account.
What we believe
- Repeatable science. A number is a result only with its interval, its
noise floor, its seed and the versions that produced it. A mean alone is
not a result. A flat result is a result. (
pass_at,eval_variance,delta_report,holdout_size.) - Replicated papers are the proof. We show the library works by
reproducing recent post-training research in it, one recipe per paper,
under an hour on one GPU, with the number it moved and the number it did
not. Every reproduced paper is a post. The proof point is the recipe,
not the pitch. (
recipes/papers/.) - The book is the map, the paper is the citation. Every default is
named, sourced and tunable from the call. rlhfbook.com
(Lambert) is the map of the field; the originating paper is the
reference. A default with no source says “convention, untested”.
(
defaults.py,scripts/check_no_hardcoding.py.) - Bring your own keys. Your models, your compute, your accounts.
Modal and Prime Intellect are first-class: a
whileaienvironment becomes averifiersenvironment and back, selected rows become a trainer’s prompt set, eval results flow back into measurement with intervals. Nothing in the loop requires our hosting. - Developer ergonomics are the product. The code reads like PyTorch,
DSPy and Unsloth: one import, objects carry configuration, calls carry
data, reports print themselves, errors name the fix, and a first-time
reader can guess the next line. Rigor lives behind a default, never
behind a flag. (
docs/reference/style.md, the ratchet test.) - Plain words, then the mechanism, then the proof. Every page, every
docstring, every README section in that order. The first thing a reader
sees is the loop as five lines, one per step, each the step’s name and
one sentence saying why the step exists in the reader’s own words
(“Measure. One run proves nothing. Ask whether a change is real or
noise before you ship it or train on it.”). Nothing goes in front of
that list: a manufactured hook (“Break it. Score it. Prove it.”) and a
paragraph about the problem both read worse than the list, and were
cut the same day they shipped. Book vocabulary (interval, rollout,
band, gradient) stays in the docstring that cites the chapter, never in
a lead sentence or a public name. The step names are Simulate, Grade,
Measure, Select, Train. Measure is evaluation with intervals and Select
is data curation; they are not merged, and neither is renamed to Eval.
The README is a front page, not a paper. It follows the skeleton the
most-used Python repos share (Polars, TRL, vLLM, uv, decomposed
2026-09-19): logo, one-line tagline, badges, link bar; the five-line
loop; Install; Quick start, offline, with its printed output; With your
agent; the call table; Why the numbers hold, as bullets of term,
mechanism, citation; Recipes; Platform; Documentation; Development;
Cite; References, collapsed; License. Prose outside code, tables and
the references stays under 900 words. A code block carries the
explanation wherever one can; anything longer than a paragraph moves
to
docs/. - Mass experimentation. A PhD or an engineer runs many experiments from one import, on their own compute, and every run leaves a record that a person can decide from.
- Never big-bang. The internals carry the science and the tests. Change the front door, migrate callers mechanically, keep the old name working for one release with a warning that says the new one.
- One name. The company is While, the package is
whileai, the import isimport whileai as wai, the command iswai(whileairuns the same entry point;zpis gone), the hosts are while.ai (site and platform), api.while.ai and docs.while.ai, the variables areWHILEAI_*, the config dir is~/.whileai. app.withwhile.com was retired the same day; nothing links to it. ZeroProof was the name before 2026-09-16; the cutover finished on 2026-09-19 and nothing new is written under it: no code, page, prompt, routine, dataset card or post. What still carries the old name is wire protocol and infrastructure that would break users if renamed (thezp_key prefix,zeroproof.*span attribute keys, Modal app hostnames, volume and table names) and the history inCHANGELOG.md. Those are pinned, not permitted:scripts/old_name_baseline.jsoncounts them per file, a count may fall and never rise, and a new file may not add one. TheZEROPROOF_*variables and~/.zeroproofare not read. - One counter. The version is
0.N. N goes up by one per release and never rolls over, resets or pads:0.99then0.100then0.101. There is no1.0; a number says how many releases came before it, nothing about maturity. PEP 440 drops leading zeros, which is why the counter is never padded (1.07is1.7on PyPI). On 2026-09-20 the bump script rolled0.99over to1.00and nothing in this file said it could not; those uploads are yanked and re-cut as0.100to0.109.