| --- |
| license: other |
| task_categories: |
| - visual-question-answering |
| tags: |
| - benchability |
| - figure4 |
| - vlm-sft |
| --- |
| |
| # BenchAbility Figure 4 -- `capability_guided` |
| |
| 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 | `capability_guided` | |
| | selection | by capability, gap-weighted from the Figure 3 scores | |
| | intervention rows | 59,999 | |
| | 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 |
|
|
| Per-capability quotas come from the measured Figure 3 scores: |
|
|
| gap(l) = max(0, 95 - score(l)) |
| weight(l) = 0.20/9 + 0.80 * gap(l) / sum(gap) |
| |
| The scores are measured on the diagnosis benchmarks through `scripts/build_fig1.py`'s own code path, |
| not the placeholder table in the plan document. The target is 95 rather than 70 because the real |
| scores top out at 93.69 -- at 70, seven of the nine capabilities have gap 0 and the experiment |
| collapses into "train two capabilities". |
|
|
| Within each capability bucket, samples are drawn **across benchmarks** under three diversity bounds: |
| a 50% per-source cap, a 15% per-template cap, and at most 2 rows per image. Where a bound is |
| arithmetically unsatisfiable it is relaxed one at a time and the relaxation is recorded -- |
| `key_information_extraction` is three templates in total by construction, and AI2D is 71% of |
| `diagram_and_infographic_understanding`. |
|
|
| Two capabilities cannot be filled from this pool: diagram (7,264 available against 11,081 requested) |
| and KIE (1,416 against 1,864). The deficit is redistributed over the capabilities that have room, in |
| gap proportion. It is **never** filled by upsampling with replacement, which would give those buckets |
| more effective epochs than the same rows get in the other arm. |
|
|
| ## 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`. |
|
|
