SP5 design covers every known adaptive-parameter deferral in the DQN
training loop in a single coherent project. After SP5: zero hardcoded
multipliers, every adaptive value ISV-driven via Pearls A+D.
9 pearls + 1 sweep close-out + 1 validation milestone:
1-3. Per-branch atom span / loss budget / NoisyNet σ (52 slots)
4. Per-group Adam β1/β2/ε ISV-driven (24 slots)
5. Per-branch IQN τ schedule (20 slots)
6. Kelly cap signal-driven floors (6 slots)
7. dist_q/h/f Bin(2,0.5) audit + action_select fix (0-8 slots)
8. Trail stop signal-driven thresholds (6-8 slots)
9. Thompson direction-branch temperature (4 slots)
1-ext. Per-branch C51 num_atoms (4 slots)
Layer A close-out: 5 host-EMA host→GPU migrations
Validation: 3-seed × 50-epoch acceptance gate
Total: 120-128 new ISV slots, ~5000-7500 LOC, 11-13 producer kernels,
~12 consumer migrations.
Layer A (additive infrastructure, ~15 commits) → Layer B (atomic
consumer migration, single coordinated commit) → Layer C (validation +
cleanup). Mirrors SP4's layer pattern.
Triggering data: train-multi-seed-cv2mw 50-epoch L40S baseline
(terminated F0 ep10) revealed magnitude head Q-flatness, eval collapse,
and frozen action distributions. Plus all SP4 close-out + sweep
deferrals folded in per user direction "no deferrals — make a single
plan based on ALL findings".
Spec at:
docs/superpowers/specs/2026-05-01-sp5-magnitude-differentiation-and-eval-collapse-design.md
User review pending before invoking writing-plans skill.
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