Previous fixes (PopArt carry-forward + iqr floor in d710f9d50) reduced
fold-1 ep-1 train loss 16× but ep-2/ep-3 still oscillated 10k-200k
while val Sharpe stayed healthy at 76 — confirming the model itself
trains fine and the inflated train_loss is a pure measurement artefact.
Root cause in mse_loss_kernel.cu:457: the Bellman target
target_q = reward + γ·(1-done)·target_eq
is NOT clamped to the C51 atom support [v_min, v_max], whereas
`online_eq` is naturally bounded (it's an expected value of softmax
probabilities over atoms in that support). When PopArt-normalised
reward lands outside support — most acutely at fold boundaries when
cached robust stats lag the new fold's distribution by one epoch —
td = online_eq - target_q
grows to O(10^3–10^4), and
mse = 0.5·td²
inflates to 10^5–10^6 per-sample, dwarfing the (correctly bounded)
C51 distributional loss in the blended warmup total
(1-α)·MSE + α·C51
C51 already handles this in c51_loss_kernel.cu:247
t_z = fminf(fmaxf(t_z, v_min), v_max)
because the categorical projection requires it. MSE was missing the
matching clamp.
Fix: one-line `target_q = fminf(fmaxf(target_q, v_min), v_max)` in
mse_loss_kernel.cu between the ensemble-disagreement adjustment and
the td compute. Mirrors C51's clamp exactly.
No information lost: anything outside [v_min, v_max] is unrepresentable
in the categorical distribution anyway, so MSE warmup tracking targets
that the network categorically CANNOT learn produces a meaningless
reading. With the clamp, train loss stays bounded by support × atoms,
matching healthy fold-0 readings.
Per pearl_blend_formulas_must_have_permanent_floor.md (numerical-
stability bound carve-out under Invariant 1).
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