| --- |
| 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 |
|
|
| ```bash |
| 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`. |
|
|
