---
license: mit
task_categories:
  - other
tags:
  - training-data-attribution
  - linear-datamodeling-score
  - nanochat
  - climbmix
---

<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>
</div>

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:

```json
{"train_subset": [pool-local indices], "train_subset_global": [corpus indices], "test_loss": [500 floats]}
```

## Usage

```python
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`:

```bash
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`](https://huggingface.co/rishitdagli/stride-nanochat).

## Citation

```bibtex
@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}, 
}
```