metadata
license: mit
task_categories:
- other
tags:
- training-data-attribution
- linear-datamodeling-score
- nanochat
- climbmix
STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations
Ground-truth Linear Datamodeling Score (LDS) targets for the four nanochat pre-training models, plus the shared held-out test set.
Each lds_<tag>.jsonl was produced by: sampling a
pool of pre-training examples, drawing 256 random 30%-subsets, training a fresh nanochat
from scratch on each subset, and recording per-example held-out test losses. The
_meta header records the pool indices so scores defined over the full corpus
can be compared to the subset losses.
Files
climbmix_test_d24.jsonl # 500 held-out test queries (shared across depths)
lds_d12.jsonl # 256 subsets x 500 test losses (+ lds_d12.meta.json)
lds_d16.jsonl
lds_d20.jsonl
lds_d24.jsonl
Each non-_meta line:
{"train_subset": [pool-local indices], "train_subset_global": [corpus indices], "test_loss": [500 floats]}
Usage
from stride.inference import Stride
attr = Stride.from_pretrained("d12")
result = attr.attribute(test_queries)
lds = attr.evaluate_lds(scores=result.scores) # downloads lds_d12.jsonl from here
print(lds["lds_spearman_mean"])
Or score an existing method's .npz:
python -m stride.cli.eval_lds --scores my_scores.npz --lds lds_d12.jsonl --method MyMethod
The models and operators live in the companion model repo
rishitdagli/stride-nanochat.
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},
}