d9a4d98a3d
Smoke smoke-test-fxvkk (commit 48c25d999, Mech 6 anchored clip) F1-NaN'd
at step 3720 with slots 36-38, 40-42 still firing — Adam EMAs saturated
despite Mech 6 capping the global clip threshold.
Diagnosis: Mech 6 bounds the AGGREGATE L2 gradient norm, but Adam m/v
EMAs are PER-ELEMENT. A gradient with one large element (e.g.,
element_X = 100, rest small) has L2 norm ≈ 100, passing a clip
threshold of 1000 untouched. Adam m_X EMA accumulates the large
element; β1=0.9 steady-state gives m_X ≈ 1000, exceeding slot 36
threshold (100 × isv).
Fix: in dqn_adam_update_kernel, after the global L2 clip is applied,
clip EACH gradient element to ±per_element_cap where:
per_element_cap = 10 × sqrt(adaptive_clip / total_params)
Rationale:
- sqrt(adaptive_clip / N) is the average per-element contribution to
the L2 norm budget
- 10× allows legitimate per-element deviations up to 10× average
- Scales with adaptive_clip — when global clip is doing its job
(Mech 6), per_element_cap is tight enough to prevent saturation
- When global clip is loose (post-fold warmup), per_element_cap
scales with it — never tighter than the global clip's intent
Together with Mech 6, prevents Adam saturation at both the aggregate
(L2 norm) and per-element levels. Closes the residual pathology after
Mech 6's partial 3060→3720 improvement.
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;