examples/inference/gradio/trackwan/app_action.py:
Bring the interactive drawing app in line with the sparse training
regime our overfit runs used (WANTRACK_SPARSE=1). New 'Track budget'
radio: 'dense' keeps the old full 50x50 grid; 'sparse' (new default)
builds a much smaller [Tpx, num_moving + num_bg, 2] tensor -- the
moving handle points from the drawn path + num_bg random background
points from the training grid (seeded by the seed slider). New
'num_bg' slider (0-60, default 20) matches WANTRACK_EXTRA_RANDOM=20.
Overlay logic updated to draw every point in sparse mode instead of
grid-strided subsampling that assumed N==2500.
examples/train/scenario/worldmodel/finetune_wantrack_synth_sparse_random_warmstart.yaml:
Test config for the curriculum idea: start from sparse-fixed@1000
and let random-sampling sparse training take over. Compared cold-
start sparse-random (~0.8%) vs warmstart (~3.2%) vs fixed (~8.8%)
divergence.
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