Single integrated trainer where 5 loss heads (BCE direction, C51
distributional Q, categorical π, scalar V, aux prof+size) sit on a
shared Mamba2+CfC encoder. Joint training with adaptive loss-balance
λ weights via the existing pearl_loss_balance_controller infrastructure.
Discrete 9-action grid shared between Q and π heads; reward = per-trade
realized PnL from LobSimCuda.
Designed in response to lob-backtest-sweep-jpdhg result (PF=0.24,
sharpe=-9.72, mono-anti-cal in conviction→PnL): the encoder learns
directional AUC but never sees PnL-aware gradient because
stop_grad_aux_to_encoder blocks the aux head's gradient. RL training
fixes this by making the encoder optimize per-trade realized PnL via
Q-head Bellman + π-head clipped surrogate.
Every hyperparameter (γ, τ, PPO clip ε, entropy coef, rollout steps,
PER α, reward scale, loss-balance λs) is ISV-driven per
pearl_controller_anchors_isv_driven and feedback_isv_for_adaptive_bounds.
12 new ISV slots (400-411) + 12 reused existing slots + 8 new controller
kernels following the Wiener-α + first-observation-bootstrap pattern.
10 phases (A-H, ~3 weeks), falsifiable gate G8 = profit_factor > 1.0
on 2M-event backtest sweep.
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