The plan head's conviction output (sigmoid, [0,1]) scales rewards.
At init, conviction ≈ 0.1 (Xavier random weights through sigmoid),
which multiplied ALL rewards by 0.1 — killing the gradient signal.
The model couldn't learn meaningful Q-values, defaulted to uniform
random action selection → 1.7M trades on 4M bars → val_Sharpe -1000.
Fix: only apply conviction scaling when readiness ≥ 0.5 (plan head
mature). Before that, conviction defaults to 1.0 (identity). This
matches the existing readiness gate on position sizing (line 1416).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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