Skip to main content

Self-Play Pretraining with Zero Data (wip)

Implementation of Self-Play Pretraining with Zero Data

Paper Review from @hu-po

Usage

import torch
from self_play_pretrain_zero_data import Brainfuck, SelfPlay, Transformer

executor = Brainfuck()

generator = Transformer(num_tokens = executor.num_tokens, dim = 512, depth = 6)
learner = Transformer(num_tokens = 256 + 1, dim = 512, depth = 6)

self_play = SelfPlay(generator, learner, executor)
self_play(epochs = 10)

torch.save(learner.state_dict(), './learner.pt')

Citations

@misc{cowsik2026selfplaypretrainingzerodata,
    title    = {Self-Play Pretraining with Zero Data},
    author   = {Aditya Cowsik and Kfir Dolev and Michael Y. Li and G. Bruno De Luca and Nourya Cohen and Noah D. Goodman and Yoav Levine},
    year     = {2026},
    eprint   = {2609.30063},
    archivePrefix = {arXiv},
    primaryClass = {cs.AI},
    url      = {https://arxiv.org/abs/2609.30063},
}
@software{Morehead_JVP_Flash_Attention_2025,
    author  = {Morehead, Alex},
    doi     = {10.5281/zenodo.17050188},
    license = {MIT},
    month   = {sep},
    title   = {JVP Flash Attention},
    url     = {https://github.com/amorehead/jvp_flash_attention},
    version = {0.14.0},
    year    = {2025}
}
@misc{bloem2025universalpretrainingiteratedrandom,
    title   = {Universal pre-training by iterated random computation},
    author  = {Peter Bloem},
    year    = {2025},
    eprint  = {2506.20057},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2506.20057},
}

Metadata

Release files for self-play-pretrain-zero-data 0.0.16

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for self-play-pretrain-zero-data 0.0.16
File Size Uploaded
self_play_pretrain_zero_data-0.0.16.tar.gz 15.8 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for self-play-pretrain-zero-data 0.0.16
File Interpreter ABI Platform
self_play_pretrain_zero_data-0.0.16-py3-none-any.whl Python 3 none any Details

Total release size: 33.3 kB

Release files / self_play_pretrain_zero_data-0.0.16.tar.gz

Download URL self_play_pretrain_zero_data-0.0.16.tar.gz
Size 15.8 kB
Tags Source
SHA-256 checksum
How to use checksums
f21ce62bd941903fc15f4c2bd6cd059423b55f75720cf0185df12a84c384d82a
BLAKE2b-256 checksum
How to use checksums
02612c0a388da9f4c240dbb4bdb3c26a8dd5a5a2491b34e49af03f5fd39aa222
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via uv/0.8.17

Release files / self_play_pretrain_zero_data-0.0.16-py3-none-any.whl

Download URL self_play_pretrain_zero_data-0.0.16-py3-none-any.whl
Size 17.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
d74ed04c348b6568535b16c946bc63e22893f98fbbabd30162e540e6c79bf5ad
BLAKE2b-256 checksum
How to use checksums
32e0b3f92fe896ae31673d32a75580fe0aa4669a8dc2ffc486625883534e3fd7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via uv/0.8.17
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page