e9096c7be1
R1: K's hardcoded shrink-and-perturb (m×0.1, v×0.01) at fold boundary
violated feedback_adaptive_not_tuned (untracked tunable knobs) AND
created a downstream pathology: tiny v_hat denominator → oversized
Adam updates 50+ steps post-reset → trunk param overshoot → save_h_s2
NaN at F1 ~step 1745 (smoke-test-bkdx5 diagnostic).
K was introduced (commit 4ef1d8ebb) BEFORE fold_warmup_factor existed
in the same commit's "K + adaptive warmup" pair. With warmup_factor
in place — ISV-driven, dampens lr+clip via lr_eff = lr_base ×
max(MIN_WARMUP_LR_FRAC, fold_warmup_factor) — K is redundant. Single
mechanism, ISV-driven, no hardcoded constants. Eliminating K leaves
m,v reset to 0 at fold boundary; warmup_factor handles cold-start.
P: expanded nan_flags_buf 16→24 with 5 GRN-stage checks
(linear_a_out, elu_out, linear_b_out, glu_sigmoid_out,
layernorm_var/out) for finer-grained source identification if R1
alone doesn't fix F1.
Predicted outcomes:
- If K's tiny-v_hat was the cause: F1 trains successfully (R1 alone)
- If different mechanism: new GRN-stage flags pinpoint which sub-
stage produces NaN, enabling layer-level fix
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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;