README.md

---
pretty_name: "DyT Composition Study Artifacts"
license: cc-by-4.0
language:
  - en
tags:
  - machine-learning
  - transformers
  - layernorm
  - dynamic-tanh
  - activation-bounding
  - reproducibility
size_categories:
  - n<1K
configs:
  - config_name: artifact_index
    default: true
    data_files:
      - split: train
        path: data/artifact_index.jsonl
---

# DyT Composition Study Artifacts

[![arXiv](https://img.shields.io/badge/arXiv-2604.23434-b31b1b.svg)](https://arxiv.org/abs/2604.23434)
[![GitHub](https://img.shields.io/badge/GitHub-code-black.svg)](https://github.com/lucky-verma/dyt-composition-study)
[![License: CC BY 4.0](https://img.shields.io/badge/License-CC--BY--4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)

This dataset contains sanitized result manifests and analysis outputs for
**[When Does Removing LayerNorm Help? Activation Bounding as a Regime-Dependent Implicit Regularizer](https://arxiv.org/abs/2604.23434)**.

DOI: https://doi.org/10.48550/arXiv.2604.23434

## Contents

The artifacts include aggregate training metrics, saturation measurements, statistical-test summaries, predictor-validation outputs, table-source manifests, and selected aggregate analysis files used by the public code repository.

The Dataset Viewer table is an index of the artifact files. The machine-readable result artifacts are stored under `results/`.

This is not a natural-language training dataset. It does not redistribute WikiText, OpenWebText, LAMBADA, BLIMP, model checkpoints, or raw training logs.

## Intended Use

Use this artifact bundle to:

- inspect the machine-readable results behind the paper;
- reproduce paper tables and consistency checks;
- compare DyT, LayerNorm, RMSNorm, HardTanh, DiffAttn, and related controls at the reported scales;
- audit provenance for reported quantitative claims.

## Limitations

- The experiments are compute-limited and below Chinchilla-optimal training.
- The included files are result artifacts, not full raw training traces or checkpoints.
- The saturation diagnostic should be treated as a per-deployment calibration cue, not a universal rule.
- Raw public datasets retain their original licenses and are not mirrored here.

## Licensing

The result artifacts in this dataset are released under CC BY 4.0.

The associated GitHub code is released under the MIT License.

## Citation

```bibtex
@misc{verma2026dytcomposition,
  title        = {When Does Removing LayerNorm Help? Activation Bounding as a Regime-Dependent Implicit Regularizer},
  author       = {Verma, Lucky},
  year         = {2026},
  publisher    = {arXiv},
  doi          = {10.48550/arXiv.2604.23434},
  url          = {https://arxiv.org/abs/2604.23434},
  eprint       = {2604.23434},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG}
}
```