Replaces random τ ∈ U(0,1) sampling with `FIXED_TAUS = [0.05, 0.25, 0.50,
0.75, 0.95]`. Kernel-side `IQN_NUM_QUANTILES` macro 32 → 5; `GpuIqnConfig::
default().num_quantiles` 32 → 5. Construction-time τ broadcast (option B2)
populates `online_taus` / `target_taus` / `cos_features` once via
`clone_htod`; both online and target IQN forwards plus the CVaR cold path
read this static buffer. The Philox-driven `iqn_sample_taus_kernel` deleted
along with its only Rust consumer (in `compute_cvar_scales`); the
`rng_step` Philox seed counter also gone. Action ranking in the IQN
inference kernel switched from mean-over-quantiles to MEDIAN
(`q_acc[a] = q_val` only when `t == IQN_MEDIAN_INDEX = 2`); the off-median
positions feed four new ISV diagnostic slots.
Four new ISV slots tail-appended:
IQN_Q_P05_EMA_INDEX = 99 (mean |Q| at τ=0.05, EMA)
IQN_Q_P25_EMA_INDEX = 100 (τ=0.25)
IQN_Q_P75_EMA_INDEX = 101 (τ=0.75)
IQN_Q_P95_EMA_INDEX = 102 (τ=0.95)
Median (τ=0.50) intentionally skipped — already in greedy-Q diagnostic.
Fingerprint pair shifted 97→103, 98→104; ISV_TOTAL_DIM 99→105.
Layout fingerprint: 0x3e21acecd922e540 → 0x5789155b683ab59c.
New kernel `iqn_quantile_ema_kernel.cu` (4-block × 256-thread shmem-reduce,
no atomicAdd) reads `save_q_online [TBA, B*Q]` and EMA-updates the four
slots. Launched from `training_loop.rs` per-step alongside
`launch_h_s2_rms_ema`. StateResetRegistry extended with 4 FoldReset
entries (cold-start 0.0).
Hyperparam plumbing: `hyperparams.num_quantiles` and
`DQNConfig::iqn_num_quantiles` pinned to `FIXED_TAUS.len()` at the
`GpuIqnConfig` construction site in `fused_training.rs::new` and
`trainer/constructor.rs`. Legacy fields stay for compat; production /
hyperopt configs (dqn-production.toml, DQNHyperparameters defaults)
aligned to 5.
Adam state for IQN params auto-resizes via `m_buf`/`v_buf` sizing
through `total_params + cublas_pad`. **Checkpoint break** — IQN head
parameter shapes change with `num_quantiles`; new fingerprint hash
fails-fast at constructor load on pre-Task-3 checkpoints.
Smoke tests:
- New `iqn_multi_quantile_heads_produce_monotonic_estimates` (1.23s on
RTX 3050 Ti): asserts ISV[99..103) finite + non-zero + spread > 1e-6
after 1 epoch — PASS (Q_p05=0.0187 Q_p25=0.0200 Q_p75=0.0193
Q_p95=0.0190).
- `multi_fold_convergence` (606.50s, 3 folds × 5 epochs): all 3 fold
checkpoints written; per-fold best train Sharpe -8.17 / 74.24 / 63.44
at epochs 2 / 4 / 2 (mean 43.17 vs 2c.3c.6 baseline mean 23.43 — folds
1+2 substantially up, fold 0 down -16 points; absolute-mean comfortably
above the plan's 3.8 floor). No NaN/Inf, no panic.
cargo check clean at 11 warnings (baseline preserved); cargo build
compiles 61 cubins (was 60; +iqn_quantile_ema, -nothing — the old
sample_taus kernel was inside iqn_dual_head_kernel.cu, not a separate
cubin file).
Files touched: 14 modified (`iqn_dual_head_kernel.cu`, `iqn_cvar_kernel.cu`,
`gpu_iqn_head.rs`, `gpu_dqn_trainer.rs`, `build.rs`, `state_reset_registry
.rs`, `training_loop.rs`, `constructor.rs`, `fused_training.rs`,
`config.rs`, `dqn-production.toml`, `smoke_tests/mod.rs`,
`docs/dqn-wire-up-audit.md`, `dqn-production.toml`) + 2 new (`iqn_quantile
_ema_kernel.cu`, `smoke_tests/iqn_quantile_monotonicity.rs`).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
005ed3a4f9