ab2133463e
Smoke smoke-test-dr2bn (commit 19b008e1c) F1-NaN'd at step 240 — earlier
than pre-fix smoke smoke-test-xvzgk (step 890). The fix made things
worse, indicating the ε floor `(1e6 × isv).max(1e3)` is actively
destabilizing F1 startup.
Diagnosis: at fold boundary, ISV[H_S2_RMS_EMA_INDEX=96] and
ISV[Q_DIR_ABS_REF_INDEX=21] reset to 0 (per StateResetRegistry).
Formula `(1e6 × 0).max(1e3) = 1e3` makes max_abs aggressively narrow.
F1 startup gradients can have natural magnitudes > 1e3 (post-fold
Bellman-target shift); clamping them to ±1e3 destabilizes Adam EMAs,
which then drive cuBLAS GEMM accumulators into pathological inputs
that overflow → slot 26 + 32 NaN.
New formula: `1e6 × isv.max(1.0)` guarantees max_abs ≥ 1e6 regardless
of ISV state:
- ISV = 0 → max_abs = 1e6 × max(0, 1.0) = 1e6
- ISV = 0.5 → max_abs = 1e6 × max(0.5, 1.0) = 1e6
- ISV = 2.0 → max_abs = 1e6 × max(2.0, 1.0) = 2e6
- ISV = 100 → max_abs = 1e8
F0 no-op intent preserved (F0 inputs ≪ 1e6 in all states; ISV[96]≈1.0
for converged F0 → max_abs = 1e6, well above F0-typical |iqn_d_h_s2|
≤ ~10²). F1 startup gradients ≤ 1e6 are un-clipped.
The 1.0 ε floor is on the ISV multiplier (Invariant 1 carve-out per
`feedback_isv_for_adaptive_bounds`), not on the bound itself — bound
is still ISV-driven when ISV is meaningful (≥ 1.0).
Two edit sites:
- gpu_dqn_trainer.rs:6967 (apply_iqn_trunk_gradient, slot 26 path)
- gpu_dqn_trainer.rs:18631 (launch_cublas_backward_to, slot 32 path)
F0 regression to 35.24 still unsolved — separate investigation thread
within SP1 (no deferral; Phase B instrumentation timing impact suspected).
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;