- a5_generalisation: connectors -> arrows (baseline->ours direction, shows the drop
and the stdout solve-cost honestly).
- equiv0 -> approx0 everywhere: these are finite-sample estimates, not identically 0.
- plot_train_vs_deploy skips when train==deploy for every run (no knob-ON contrast);
fixes the 'can't see train' longrun/sub4 figures (they had no hk_on data).
- Prune 9 orphan figure sets not referenced in paper or blog (regenerable on demand);
keep the 3 referenced + a5 + train_vs_deploy_60_train_deploy. All 4 CSVs committed.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
A vanilla seed (s43) lacked the held-out deploy eval, so its train series fell
back to the noisy n=28 per-step hack_s while other seeds used the n=64 eval.
Averaging mixed estimators fabricated a vanilla train-vs-deploy gap that does
not exist (lie-factor). Now: train series reuses the knob-off eval only (nan if
absent -> seed drops from the mean), and missing eval columns normalise to nan
so absent==all-nan. Regenerated all figures from logs. The canonical
train_vs_deploy_60 (has hk_on) is unchanged; sub4/longrun byproducts now show
train==deploy honestly (no knob-on data to split).
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
wassname