| --- |
| 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](https://huggingface.co/datasets/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](https://arxiv.org/abs/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](https://marin.community/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](https://github.com/marin-community/speedrun). |
|
|
| ### `regmix/` (350 KB, MIT, from Sea AI Lab) |
| Final-point-only CSVs from [RegMix, arXiv:2407.01492](https://arxiv.org/abs/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. |
|
|
