---
license: cc-by-4.0
task_categories:
- text-generation
tags:
- scaling-laws
- language-models
- experimental-design
---

# Public-checkpoint reanalysis: normalised run tables

Normalised training-loss observations from published language-model studies, used for the
public-checkpoint reanalysis accompanying *Tokens-per-Parameter Coverage Is Critical for Robust
LLM Scaling Law Extrapolation*.

Every number in that analysis is recomputable from these tables alone, with no need to re-fetch
the upstream sources.

## Files

| File | Rows | Contents |
|---|---|---|
| `runs_expanded.csv` | 12,531 | Full corpus: all ten qualifying studies, including mid-training checkpoints |
| `runs_normalised.csv` | 753 | Terminal-observation corpus used for the primary analysis |
| `datadecide_ppl_by_group.csv` | - | DataDecide per-group perplexities with reconstructed token counts |
| `farseer_1222_full.csv` | - | Farseer released run table |

## Schema

`cell` (study/corpus identifier), `run_id`, `N` (non-embedding parameters), `D` (training
tokens), `loss`, `loss_smooth`, `loss_val` where released, and `k = D/N` (tokens per parameter).

## Provenance

Each row derives from the releasing study's own published loss data. Model size and token count
are recoverable per run; studies where either could not be recovered, or which span too few
distinct model sizes to carve a design from, were screened out and are logged with the
requirement they failed in the accompanying analysis package.

Included studies: Farseer, Chinchilla (isoFLOP, digitized), Gemstones (annealed and constant-LR),
Gadre et al. over-training, the OLMo ladder, DataDecide, MAD, GPT-3 curves, and OPT trajectories.

The ColPret aggregation (Choshen et al.) is a source for several of these; it is not
redistributed here, and the fetch script in the analysis package pulls it from the original
release.

## Reproducing

The analysis code, the pre-registered protocol with its dated amendments, and the screening log
accompany the paper. Point the analysis at these tables in place of the fetch step.