Store a quantile grid instead of a raw reservoir sample in the setup cache

NormalizerEntry.energy_reservoir_sample kept 100k raw energy values purely
to seed EnergyRouter centers via np.quantile at load time, which alone
accounted for most of the setup cache sidecar's ~2MB size (float32 values
round-tripped through Python floats serialize at full double precision).
Only a handful of quantile levels are ever read back, so collapse the
sample to a fixed 1001-point quantile grid at save time and interpolate
arbitrary levels from it at use time instead — about 100x smaller with
negligible (<0.001) error on the levels that matter. Bumps the cache
format version since old sidecars have no such grid to fall back on.

scripts/warm_setup_cache.py

@@ -30,7 +30,7 @@ def run_warm_setup_cache(
     `giant train` invocation will use so it hits this warmed entry.
     `router_enabled`/`router_type`/`n_experts` only matter for
     `router_type == "process"` (warms that `n_experts`'s process map); the
-    energy-router reservoir sample is always collected regardless, so a
+    energy-router quantile summary is always collected regardless, so a
     later `--router-type energy` run never needs to rescan just to seed
     centers.
     """