Datasets:
license: other
task_categories:
- visual-question-answering
tags:
- benchability
- figure4
- vlm-sft
BenchAbility Figure 4 -- source_uniform
One of two training mixtures drawn from the same frozen 884,143-row candidate pool, with the same budget (60,000 intervention + 15,000 shared replay) and the same hyperparameters. The two differ only in how the samples are chosen, which is the whole experiment.
| arm | source_uniform |
| selection | by source provenance only, chart:doc:ocr = 3:4:4 |
| intervention rows | 60,048 |
| replay rows | 15,000 |
| shards | 38 |
| pool | 884,143 rows / 20 source datasets, capability-tagged sample-by-sample |
Columns
| column | meaning |
|---|---|
uid |
source-split-index, stable across both arms |
source |
original dataset (provenance) |
capability |
BenchAbility leaf, assigned per sample by a vision-language classifier |
split_role |
intervention or replay |
question / answer |
the training turn; <image> marks where the image goes |
image |
PNG bytes, embedded |
capability is present in both arms so the mixtures can be compared, but the
source_uniform draw never read it -- see below.
How this arm was drawn
Sources are grouped into the three coarse families Figure 2 reports
(chart, doc, ocr) and drawn 3:4:4. Within a family the quota is split across sources
proportional to sqrt(rows), then water-filled -- not equally, because equal shares would need FUNSD
(149 rows) roughly 11 times over while the chart family never repeated a row, and unequal repetition
between the arms would confound the comparison.
This arm never reads a capability label. The draw is handed rows with the field stripped. The arm exists to model an engineer who has only benchmark-level reporting; letting it see sample-level labels would make it a weaker copy of the other arm rather than the alternative it represents. Labels are attached afterwards, for auditing what the draw happened to contain.
Reproducing
python fig4_training/pipeline/40_mix.py # both arms from the frozen pool
python fig4_training/pipeline/50_export.py # this bundle
Full draw record, including every relaxed constraint and every shortfall, is in
mixture_manifest.json.