Brainstorm spec for SP11. Resolves the policy-stagnation pathology
surfaced in T10 train-multi-seed-xkjkb seed-0 ep0-14: model finds a
stable fixed point at ep1 (peak val sharpe 80.61), then OVERFITS to
it across remaining epochs (decline 80.61 → 70.58). Q-values grow but
val performance declines because reward function has no improvement
pressure.
Architecture (every input ISV-driven):
- Z-score-driven adaptation (no hardcoded "improving" threshold):
improvement_z = val_sharpe_delta_ema / max(val_sharpe_std_ema, EPS)
- 10 ISV outputs: 6 component weights + curiosity_pressure +
saboteur_intensity_mult + adaptive weight_floor + curiosity_bound
- 5 ISV canaries: val_sharpe_delta + val_sharpe_std (Z-score noise
estimate) + 6 per-component grad ratios + saboteur engagement +
PnL magnitude EMA (signal-relative curiosity bound)
- 4 new producer kernels (controller + 3 canary computers)
- Audit + migrate hardcoded cf_weight=0.3 in mse_loss_kernel.cu:318
and c51_loss_kernel.cu:789, plus other shaping multipliers
- NEW reward dimension: curiosity bonus, bounded by PnL magnitude
Per pearl_controller_anchors_isv_driven: every threshold replaced with
sigmoid(z) — no constants encode "what counts as improving". Per
pearl_blend_formulas_must_have_permanent_floor: every weight has
adaptive floor preventing zero-out. Per pearl_engagement_rate_self_
correction: saboteur intensity self-corrects via engagement rate canary.
Per pearl_cold_start_exit_signal_or: improvement signal OR'd from
multiple canaries so single-signal-failure doesn't stall controller.
New pearl authored alongside spec: pearl_reward_as_controlled_subsystem
— meta-principle that every reward path degree of freedom is a unified
controller output. Subsumes controller-anchor pearl at the reward layer.
Scope: 20 ISV slots, 4 producer kernels, audit + migration of
hardcoded reward shaping constants, 1 atomic commit.
~1300-1700 LOC; ~2.5-3 hours subagent work.
Success metric: val_sharpe[ep20] > val_sharpe[ep1] (Fix 33-38 baseline
peaked at ep1; SP11 should shift peak later as model continues
learning).
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