license: odc-by
language:
- en
tags:
- scaling-laws
- training-curves
- benchmark
pretty_name: fast-feedback offline benchmark data
fast-feedback: offline benchmark data for cheap scaling-recipe ranking
Curated snapshot (2026-08-11) of public training-curve datasets used to build an offline ground-truth benchmark for evaluating cheap feedback mechanisms that rank LLM scaling recipes / architectures without training full ladder families.
A feedback mechanism is a budgeted procedure ("train these small rungs to N tokens, inspect the curves, output a ranking"). This data lets such procedures be evaluated by simulation: replay stored curves truncated to the requested token budget and charge the FLOPs that would have been spent, then score the predicted ranking against larger-scale ground truth.
Contents
datadecide-eval-results/ (693 MB, ODC-BY, from AI2)
Mirror of allenai/DataDecide-eval-results
(auto-converted parquet). 1,410,750 rows: per-checkpoint OLMES eval metrics for
25 data recipes × 14 model sizes (4M–1B) × 3 seeds, all trained at 100 tokens/param.
Keyed by (params, data, task, step, seed, chinchilla, tokens, compute); metrics is a
dict string incl. primary_metric, per-answer-ranking accuracies, bits_per_byte_corr.
Paper: DataDecide (ICML 2025), arXiv:2504.11393.
datadecide_checkpoint_audit.json / _summary.csv
Our audit of all 350 allenai/DataDecide-* model repos (branch-per-checkpoint):
per (recipe, size, seed) sorted step lists. Verdict: grid 100% complete; identical
1,250-step grid (2,500 at 1B) across recipes; aux seeds stop early at 530M (89%) and
750M (44%); many 150M/300M aux runs extended far beyond the standard budget.
marin_wandb/ (601 MB, from Marin/Stanford CRFM, Apache-2.0 project)
Full unsampled per-step W&B training histories for 117/118
Marin Speedrun runs (1 run private):
<family>/<run>.parquet + .meta.json (config/summary) + manifest.json.
26 variation families incl. 10 optimizer-scaling ladders × 4 matched rungs
(~154M/300M/602M/1.43B) on Llama/Qwen3 backbones. train/loss at every step;
Paloma evals (incl. eval/paloma/c4_en/bpb) every 1,000 steps; LR, token counters,
grad norms. 1,628,996 rows total.
marin_speedrun_runs.json
Leaderboard snapshot (118 runs, W&B links, submitter metadata) from marin-community/speedrun.
regmix/ (350 KB, MIT, from Sea AI Lab)
Final-point-only CSVs from RegMix, arXiv:2407.01492: mixture weights + final Pile-domain val losses for 512+256 × 1M proxies, 256 × 60M proxies, and 64 random-mixture 1B models. No curves exist publicly (verified: W&B private, proxy weights unreleased, 1B repos final-only) — usable as a final-point transfer test only.
Licenses / attribution
- DataDecide: eval data ODC-BY (attribution: Allen Institute for AI), code/models Apache-2.0.
- Marin: project is open (Apache-2.0); histories retrieved from the public W&B project
marin-community/marin(attribution: Marin community / Stanford CRFM & contributors). - RegMix: MIT (attribution: Sea AI Lab).
This repo redistributes with attribution under the most restrictive component, ODC-BY.
Provenance
Assembled by the fast-feedback project (KRAFTON deep-learning/coreresearch).
Verification methodology and known caveats: docs/data-survey.md in the project repo.