Self-Play Pretraining with Zero Data (wip)
Implementation of Self-Play Pretraining with Zero Data
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
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| self_play_pretrain_zero_data-0.0.16-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 33.3 kB
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