Datasets:

sijieli
/

scalebench

license: cc-by-4.0
language:
  - en
pretty_name: Budget-Efficient Scaling Law Fitting Benchmark
task_categories:
  - tabular-regression
tags:
  - scaling-laws
  - active-learning
  - experimental-design
  - benchmark
  - language-modeling
size_categories:
  - 1K<n<10K
dataset_info:
  - config_name: data_constrained_scaling_law
    features:
      - name: group
        dtype: string
      - name: unique_tokens
        dtype: float64
      - name: params
        dtype: float64
      - name: tokens
        dtype: float64
      - name: loss
        dtype: float64
    splits:
      - name: train
        num_examples: 161
      - name: test
        num_examples: 21
  - config_name: domain_mixture_scaling_law
    features:
      - name: group
        dtype: string
      - name: proportion_domain_1
        dtype: float64
      - name: proportion_domain_2
        dtype: float64
      - name: proportion_domain_3
        dtype: float64
      - name: proportion_domain_4
        dtype: float64
      - name: proportion_domain_5
        dtype: float64
      - name: loss_domain_1
        dtype: float64
      - name: loss_domain_2
        dtype: float64
      - name: loss_domain_3
        dtype: float64
      - name: loss_domain_4
        dtype: float64
      - name: loss_domain_5
        dtype: float64
    splits:
      - name: train
        num_examples: 80
      - name: test
        num_examples: 24
  - config_name: farseer_scaling_law
    features:
      - name: group
        dtype: string
      - name: 'N'
        dtype: float64
      - name: D
        dtype: float64
      - name: loss
        dtype: float64
    splits:
      - name: train
        num_examples: 404
      - name: test
        num_examples: 7
  - config_name: lr_bsz_scaling_law
    features:
      - name: group
        dtype: string
      - name: lr
        dtype: float64
      - name: bsz
        dtype: float64
      - name: data_size
        dtype: float64
      - name: non_embedding_param_size
        dtype: float64
      - name: lm_loss
        dtype: float64
    splits:
      - name: train
        num_examples: 2702
      - name: test
        num_examples: 117
  - config_name: moe_scaling_law
    features:
      - name: group
        dtype: string
      - name: num_experts
        dtype: float64
      - name: dense_parameter_count
        dtype: float64
      - name: loss_validation
        dtype: float64
    splits:
      - name: train
        num_examples: 193
      - name: test
        num_examples: 28
  - config_name: parallel_scaling_law
    features:
      - name: num_params
        dtype: int64
      - name: parallel_size
        dtype: int64
      - name: group
        dtype: string
      - name: loss
        dtype: float64
    splits:
      - name: train
        num_examples: 36
      - name: test
        num_examples: 12
  - config_name: sparsity_scaling_law
    features:
      - name: group
        dtype: string
      - name: P
        dtype: float64
      - name: N_active
        dtype: float64
      - name: N_dense
        dtype: float64
      - name: D1
        dtype: float64
      - name: D2
        dtype: float64
      - name: loss
        dtype: float64
    splits:
      - name: train
        num_examples: 70
      - name: test
        num_examples: 18
  - config_name: vocab_scaling_law
    features:
      - name: group
        dtype: string
      - name: non_vocab_parameters
        dtype: float64
      - name: vocab_size
        dtype: float64
      - name: num_characters
        dtype: float64
      - name: unigram_normalized_loss
        dtype: float64
    splits:
      - name: train
        num_examples: 1080
      - name: test
        num_examples: 120
configs:
  - config_name: data_constrained_scaling_law
    data_files:
      - split: train
        path: data_constrained_scaling_law/train-*
      - split: test
        path: data_constrained_scaling_law/test-*
  - config_name: domain_mixture_scaling_law
    data_files:
      - split: train
        path: domain_mixture_scaling_law/train-*
      - split: test
        path: domain_mixture_scaling_law/test-*
  - config_name: farseer_scaling_law
    data_files:
      - split: train
        path: farseer_scaling_law/train-*
      - split: test
        path: farseer_scaling_law/test-*
  - config_name: lr_bsz_scaling_law
    data_files:
      - split: train
        path: lr_bsz_scaling_law/train-*
      - split: test
        path: lr_bsz_scaling_law/test-*
  - config_name: moe_scaling_law
    data_files:
      - split: train
        path: moe_scaling_law/train-*
      - split: test
        path: moe_scaling_law/test-*
  - config_name: parallel_scaling_law
    data_files:
      - split: train
        path: parallel_scaling_law/train-*
      - split: test
        path: parallel_scaling_law/test-*
  - config_name: sparsity_scaling_law
    data_files:
      - split: train
        path: sparsity_scaling_law/train-*
      - split: test
        path: sparsity_scaling_law/test-*
  - config_name: vocab_scaling_law
    data_files:
      - split: train
        path: vocab_scaling_law/train-*
      - split: test
        path: vocab_scaling_law/test-*

Budget-Efficient Scaling Law Fitting Benchmark

This repository contains the scaling-law benchmark dataset used in Spend Less, Fit Better: Budget-Efficient Scaling Law Fitting via Active Experiment Selection.

The benchmark is designed for budget-aware sequential experimental design in scaling-law fitting. Each configuration provides a finite pool of candidate experiments, a held-out high-cost target region, task-specific covariates, observed outcomes, and companion scaling-law definitions in laws.py.

Dataset Summary

The dataset contains 8 tabular regression tasks and 65 scaling-law instances. The tasks cover language-model scaling settings including pre-training hyperparameter tuning, data allocation, vocabulary design, domain mixture optimization, mixture-of-experts design, sparsity, parallel/inference-time scaling, and Farseer-style dense pre-training scaling.

Each task is stored as a separate Hugging Face configuration with train and test splits:

Config	Train	Test	Feature columns	Target column(s)	Law instances
`data_constrained_scaling_law`	161	21	`unique_tokens`, `params`, `tokens`	`loss`	10
`domain_mixture_scaling_law`	80	24	`proportion_domain_1` ... `proportion_domain_5`	`loss_domain_1` ... `loss_domain_5`	10
`farseer_scaling_law`	404	7	`N`, `D`	`loss`	1
`lr_bsz_scaling_law`	2702	117	`lr`, `bsz`, `data_size`, `non_embedding_param_size`	`lm_loss`	10
`moe_scaling_law`	193	28	`num_experts`, `dense_parameter_count`	`loss_validation`	10
`parallel_scaling_law`	36	12	`num_params`, `parallel_size`	`loss`	10
`sparsity_scaling_law`	70	18	`P`, `N_active`	`loss`	4
`vocab_scaling_law`	1080	120	`non_vocab_parameters`, `vocab_size`, `num_characters`	`unigram_normalized_loss`	10

The group column identifies a task-specific subproblem or grouping. For example, domain-mixture rows are grouped by model scale, and parallel-scaling rows are grouped by evaluation corpus.

Loading

from datasets import load_dataset

ds = load_dataset("sijieli/scalebench", "lr_bsz_scaling_law")
print(ds)
print(ds["train"][0])

To load a local checkout before uploading:

from datasets import load_dataset

ds = load_dataset(
    "parquet",
    data_files={
        "train": "lr_bsz_scaling_law/train-*.parquet",
        "test": "lr_bsz_scaling_law/test-*.parquet",
    },
)

Intended Use

This benchmark is intended for evaluating experiment-selection and active experimental-design methods for scaling-law fitting under budget constraints. A typical episode treats the train split as the candidate pool of runnable experiments and the test split as the target region for extrapolation evaluation.

The benchmark can be used to compare methods that:

choose experiments sequentially under a cost budget;
fit nonlinear scaling laws from sparse observations;
extrapolate to held-out high-cost regions;
optimize target-region prediction quality rather than in-sample fit.

Cost Proxies

The paper uses task-specific cost proxies to model heterogeneous experiment costs. The implementation in registry.py defines the default proxies:

Config	Cost proxy
`data_constrained_scaling_law`	`6 * params * tokens`
`domain_mixture_scaling_law`	`1`
`farseer_scaling_law`	`6 * N * D`
`lr_bsz_scaling_law`	`6 * non_embedding_param_size * data_size`
`moe_scaling_law`	`dense_parameter_count * num_experts`
`parallel_scaling_law`	`num_params`
`sparsity_scaling_law`	`6 * N_dense * D1 + 6 * N_active * D2`
`vocab_scaling_law`	`non_vocab_parameters * num_characters`

Scaling-Law Definitions

Each task directory includes a laws.py file containing the parametric scaling-law families used in the benchmark. The functions are named sl_1, sl_2, etc., and each file exposes:

LAW_REGISTRY: mapping from law ID to callable;
PARAM_COUNTS: number of free parameters for each law;
parameter bounds used by the fitting code.

These files are included to make the dataset self-contained for reproducing the benchmark protocol.

Citation

If you use this benchmark, please cite:

@misc{li2026spendlessfitbetter,
  title={Spend Less, Fit Better: Budget-Efficient Scaling Law Fitting via Active Experiment Selection},
  author={Sijie Li and Shanda Li and Haowei Lin and Weiwei Sun and Ameet Talwalkar and Yiming Yang},
  year={2026},
  eprint={2604.22753},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2604.22753}
}

License

This dataset card follows the license metadata declared for this repository. Users should also respect the licenses and terms of the original data sources referenced by the paper.