README.md

metadata

license: apache-2.0
task_categories:
  - other

IsoFLOP Scaling Law Experiments

Curated collection of IsoFLOP curve data from 6 experiments, standardized to a common schema.

This dataset is associated with the paper Problems with Chinchilla Approach 2: Systematic Biases in IsoFLOP Parabola Fits.

• Project Page: https://openathena.ai/scaling-law-analysis
• Data Extraction & Prep: Open-Athena/scaling-law-analysis
• Scaling Law Estimation: Open-Athena/vpnls

Schema

Field	Type	Description
source	string	Data source identifier. One of: ml_scalefit, epochai_chinchilla, llama_3, marin_202603, misfitting.
dataset	string	Training dataset. One of: massivetext, llama_3, comma, dclm, nemotron, fineweb_c4.
model	string	Model architecture. One of: chinchilla, llama_3, llama_2, transformer.
experiment	string	Canonical identifier. Defined as source__dataset__model with deduplication.
tokens	float	Training tokens (D). Either from the source data or derived via D = C / (6N).
params	float	Model parameter count (N). Either from the source data or derived via N = C / (6D).
budget	float	Compute budget in FLOPs (C).
loss	float	Validation loss. Underlying source varies by experiment.

Each row is uniquely identified by (experiment, tokens, params, budget).

Usage

Fit Chinchilla scaling law parameters from this dataset using vpnls:

from datasets import load_dataset
from vpnls.api import fit_vpnls

N, D, L = (
    load_dataset('open-athena/isoflop-experiments', split='train').to_pandas()
    .query("experiment == 'ml_scalefit__massivetext__chinchilla'")
    .filter(items=['params', 'tokens', 'loss']).values.copy().T
)

result = fit_vpnls(N, D, L)
print(f'α={result.alpha:.4f}, β={result.beta:.4f}, E={result.E:.4f}, A={result.A:.4f}, B={result.B:.4f}')
# α=0.3900, β=0.4300, E=1.9160, A=999.8009, B=7944.6131

See vpnls#usage for more examples.

Summary

Experiment	Points	Budgets	Reference	Collection Method
ml_scalefit__massivetext__chinchilla	124	9	arxiv:2507.09404	GitHub CSV
epochai_chinchilla__massivetext__chinchilla	123	9	arxiv:2404.10102	SVG digitization
llama_3	133	10	arxiv:2407.21783	SVG digitization
marin_202603__comma__llama_2	85	7	W&B report	W&B export
marin_202603__dclm__llama_2	85	7	W&B report	W&B export
marin_202603__nemotron__llama_2	88	8	W&B report	W&B export
misfitting__fineweb_c4__transformer	176	26	arxiv:2502.18969	Checkpoint interpolation
Total	814

Experiment Details

ml_scalefit

Chinchilla training data from Besiroglu et al. (arxiv:2507.09404). Raw data: apple/ml-scalefit/data/chinchilla.csv with columns model_size (N), n_tokens (D), loss. Budget C = 6ND is computed and snapped to the 9 Chinchilla IsoFLOP levels (6e18 to 3e21); points >10% from the nearest budget are discarded. N, D, and loss are kept as-is.

epochai_chinchilla

Independent extraction of the same Chinchilla experiments by Besiroglu et al. (arxiv:2404.10102), digitized from SVG figures in the original paper. Raw data: epoch-research/analyzing-chinchilla/data/svg_extracted_data.csv with columns Model Size (N), Training FLOP (C), loss. N and C are rounded to integers (SVG artifact). C is snapped to the same 9 budgets as ml_scalefit; near-duplicates from SVG extraction are resolved by keeping the point closest to the target budget. D is derived as C / (6N).

llama_3

Digitized from SVG figures in the Llama 3 technical report (arxiv:2407.21783). Raw data: eric-czech/llama3_isoflop_extraction/isoflops_points.csv with columns compute_budget (C), training_tokens (D), validation_loss. N is derived as C / (6D).

misfitting

Scaling law survey data from Marghi et al. (arxiv:2502.18969). Transformers trained on FineWeb, evaluated on C4. Raw data: hadasah/scaling_laws/data/scaling_results.csv — per-checkpoint training logs. IsoFLOP curves are constructed by: (1) building a grid of 40 log-spaced budget candidates, keeping levels where ≥3 model sizes have data within 10% FLOP tolerance; (2) interpolating each run's loss at target budgets via log-log interpolation over nearby checkpoints; (3) selecting the best learning rate per model size. D is derived from the target budget. Follows the interpolation approach in hadasah/scaling_laws/paper_analysis_and_plots.py.

marin_202603

Marin community scaling ladder experiments: Llama 2 models trained on three datasets (Comma, DCLM, Nemotron). Raw data: vendored CSVs exported from the Marin W&B project. Budget is parsed from run names and multiplied by 3 to convert from forward-pass FLOPs (≈2ND) to total FLOPs (≈6ND); this factor was validated empirically across all runs. "Validation-optimal" runs (which use a different FLOPs convention) are excluded. Loss is eval/paloma/macro_loss.

Citation

@article{openathena2026approach2,
  title={Problems with Chinchilla Approach 2: Systematic Biases in IsoFLOP Parabola Fits},
  author={Czech, Eric and Xu, Zhiwei and Elmatad, Yael and Wang, Yixin and Held, William},
  journal={arXiv preprint arXiv:2603.22339},
  year={2026}
}