feat(dqn-v2): B.3 + C.5 GPU-only replay seed warm-start + CQL α ramp

Plan 3 Tasks 8 + 9. Single commit because Task 9 directly consumes Task 8's
seed-fraction signal; no useful intermediate state.

ISV tail-append:
- [82] SEED_STEPS_TARGET_INDEX — config replay_seed_steps (CPU constructor write)
- [83] SEED_STEPS_DONE_INDEX — GPU-incremented per collect_experiences_gpu
- [84] SEED_FRAC_EMA_INDEX — adaptive EMA of (1 - done/target)
- Fingerprint shifted [80,81] → [85,86]; ISV_TOTAL_DIM 82 → 87

GPU-only design (per user direction "fully gpu driven no cpu involvement"):
- 4 scripted policies as ONE CUDA kernel (scripted_policy_kernel.cu)
- Per-sample policy mix (40% uniform LCG / 20% momentum / 20% mean-rev /
  20% vwap-deviation) deterministic by `i % 5`
- Action source switched at launch boundary (CPU per-epoch read of ISV slot
  decides which kernel to dispatch; the action computation itself is 100% GPU)
- No CPU physics mirror — existing GPU `experience_env_step` runs unchanged

seed_step_counter_update_kernel.cu:
- Single-thread cold-path; increments DONE, computes FRAC = max(0, 1-done/target)
- Adaptive α matches Task 3/4 convention (α_base × (1 + 0.5×|clamp(sharpe,±2)|))

cql_alpha_seed_update_kernel.cu (Task 9):
- target = config.cql_alpha × max(0, 1 - seed_frac)
- During seed phase (frac=1) → target=0 → CQL α decays to 0 (no pessimism on
  exploration data); as frac → 0 → CQL α ramps to config value
- Updates ISV[CQL_ALPHA_INDEX=48]; CQL gradient kernel reads slot 48 via
  pinned device-mapped ISV (Plan 1 Task 12 consumer pattern unchanged)

Registry: SEED_STEPS_DONE + SEED_FRAC_EMA both FoldReset; CQL_ALPHA flipped
SchemaContract → FoldReset; SEED_STEPS_TARGET stays SchemaContract.

Read-only monitors (mirror PlanThresholdMonitor / StateKlMonitor pattern):
- monitors/seed_monitor.rs — surfaces ISV[82..85) for HEALTH_DIAG +
  controller_activity smoke fire-rate
- monitors/cql_alpha_monitor.rs — surfaces ISV[48] + ISV[84] dependency

Smoke (RTX 3050 Ti, 3 folds × 5 epochs, dqn-smoketest profile with
replay_seed_steps=1000 override so seed phase completes mid-fold):
- All 3 folds saved best-checkpoint
- Fold 2 best Sharpe = 92.4938 at epoch 1 (target range 80-120) ✓
- Per-fold val_metric: f0=3.80 / f1=9.73 / f2=20.24 (loss-based)
- HEALTH_DIAG[3..4] cql_alpha=0.0500 with health=0.49 → base ≈ 0.10 from ISV[48],
  consistent with kernel ramping toward final×(1-frac); regime gate
  (1-regime)×health applies on top
- 11 cargo check warnings (matches pre-task baseline; no new warnings)
- 6/6 monitor unit tests pass (read/diagnose/observe×fire_rate)

Smoke override rationale: smoke runs ~200 samples per collect (4 episodes ×
50 timesteps) × 5 epochs × 3 folds ≈ 3000 total. Default 100k target would
keep entire smoke in seed phase. Override to 1000 lets the seed→network
transition complete mid-fold so the CQL α ramp is observable.

Per pearl_one_unbounded_signal_per_reward.md: cql_alpha is bounded (clamped
to config_final × (1 - seed_frac) ∈ [0, config_final]), composes safely with
downstream CQL loss.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

config/training/dqn-smoketest.toml

@@ -49,6 +49,12 @@ noise_sigma = 0.1
 buffer_size = 256
 min_replay_size = 64
 regime_replay_decay = 0.3
+# Plan 3 Task 8 B.3: smoke override. Production default 100_000; smoke runs
+# only ~200 samples per `collect_experiences_gpu` (4 episodes × 50 timesteps)
+# × 5 epochs × 3 folds ≈ 3000 total. Setting to 1_000 lets the seed phase
+# complete mid-fold so the seed→network transition is observable in the smoke
+# (HEALTH_DIAG cql_alpha.eff visibly ramps from 0 toward config.cql_alpha).
+replay_seed_steps = 1000
 
 [early_stopping]
 enabled = true