---
license: apache-2.0
language:
- en
tags:
- transformers
- residual-connections
- scaling-laws
- language-modeling
- reliability
pretty_name: "Review Residuals — Scaling Sweep Results"
size_categories:
- n<1K
---

# Review Residuals — Scaling Sweep Results

Supporting data, code, and paper for **"Review Residuals: Update-Conditioned Residual Gating for Transformers"** (Kyle Kramer, NeraTech LLC, 2026).

Review Residuals scale each transformer sublayer's proposed update by a small learned gate conditioned on *both* the current state **and** the proposed update — an in-network analogue of *independent verification*. Trained from scratch with parameter-matched baselines, the advantage over both a Highway gate and the standard residual **emerges with scale**.

**Code & full repo:** https://github.com/SixSigmaEngineer/review-residuals

## What's here

- `scaling_v8.csv` — the 42 training runs (per-seed validation losses) behind every number in the paper.
- `train.py`, `analyze_results.py`, `make_emergence_figure.py`, `run_on_runpod.ipynb` — training, analysis, figure, and cloud-GPU code.
- `review_residuals.pdf` — the technical paper; plain-English companion included.
- Figures: `mechanism_additive.png`, `emergence_curve.png`.

## `scaling_v8.csv` schema

| column | meaning |
|---|---|
| `size` | model size group (60M, 150M, 320M, 590M, 1B) |
| `variant` | `review_neutral` (ours), `highway`, or `standard` (both baselines parameter-matched up to Review) |
| `seed` | random seed |
| `params_M` | actual parameter count (millions) |
| `steps` | training steps |
| `val_loss` | validation loss (lower is better) |
| `ece` | expected calibration error (where measured) |
| `minutes` | wall-clock training time |

## Headline result

| Size | Review | Highway | Standard | Review wins? |
|---|:--:|:--:|:--:|:--|
| 60M | 1.6891 | 1.6923 | **1.6805** | no |
| 150M | 1.5576 | 1.5625 | 1.5548 | tie |
| 320M | **1.5001** | 1.5042 | 1.5037 | tie |
| 590M | **1.4795** | 1.4889 | 1.4901 | **yes (p<0.05)** |
| 1B | **1.4876** | 1.5031 | 1.5040 | **yes (strong trend)** |

Reproduce the table and t-tests with `python analyze_results.py`.

## Citation

```bibtex
@misc{kramer2026reviewresiduals,
  title  = {Review Residuals: Update-Conditioned Residual Gating for Transformers},
  author = {Kramer, Kyle},
  year   = {2026},
  note   = {NeraTech LLC}
}
```

Licensed under Apache-2.0.