stride-nanochat

<div align="center"> <h2>STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations</h2>

<a href="https://arxiv.org/abs/2606.05165"><img src='https://img.shields.io/badge/arXiv-STRIDE-red' alt='Paper'></a> <a href='https://stride-tda.github.io'><img src='https://img.shields.io/badge/Project_Page-STRIDE-green' alt='Project Page'></a> <a href='https://huggingface.co/rishitdagli/stride-nanochat'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20-Models-yellow'></a> <a href='https://huggingface.co/datasets/rishitdagli/stride-lds'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20-LDS%20Dataset-yellow'></a>

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Trained STRIDE steering operators and the nanochat base checkpoints they attribute, for the four pre-training models in the paper. STRIDE attributes a model's prediction back to the pre-training examples that shaped it, by learning a tiny activation-steering operator instead of retraining.

Layout

base_checkpoints/<tag>/model_<step>.pt   # nanochat base checkpoint
base_checkpoints/<tag>/meta_<step>.json  # nanochat config
operators/<tag>/operator.pt     # trained SteeringOperator state dict
operators/<tag>/subsets.npy     # subset membership (K=1000, d=10)
operators/<tag>/meta.json       # training config + n_train + operator dims
tokenizer/                      # shared nanochat tokenizer

Usage

from stride.inference import Stride
attr = Stride.from_pretrained("d12")
result = attr.attribute(my_queries)

The LDS ground-truth losses and the held-out test set live in the dataset repo rishitdagli/stride-lds.

Citation

@misc{dagli2026stridetrainingdataattribution,
      title={STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations}, 
      author={Rishit Dagli and Abir Harrasse and Luke Zhang and Florent Draye and Amirali Abdullah and Bernhard Schölkopf and Zhijing Jin},
      year={2026},
      eprint={2606.05165},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2606.05165}, 
}