Split CUDA Graph (forward + adam phases with gradient injection point):
- IQN trunk gradient flows through single Adam (no dual optimizer conflict)
- Spectral norm runs BEFORE forward (not after Adam — no tug-of-war)
- σ_max in 40D hyperopt search space [1.0, 10.0]
Attention Phase B backward:
- Full gradient flow through 4-head self-attention weights
- Separate Adam optimizer for attention params
- Backward kernel recomputes forward from saved_input (memory-efficient)
Ensemble multi-head:
- Real cuBLAS value head forward per ensemble head (was copying head 0 logits)
- KL diversity gradient kernel with hierarchical reduction
- forward_value_head() on CublasForward for per-head SGEMM
Regime PER scaling:
- Kernel reads target ADX/CUSUM from states_buf directly (zero CPU readback)
- Removed 2x memcpy_dtoh per training step
Decision Transformer:
- 14 CUDA kernels (embed, causal attention, FFN, CE loss + backward + trajectory building)
- GPU-native trajectory builder (return-to-go reverse cumsum, momentum expert actions)
- Wired into training loop with dt_pretrain_epochs config
HER Future/Final:
- episode_ids flow through PER buffer (GpuBatch, GpuReplayBuffer, GpuBatchSlices)
- GPU-native donor sampling (binary search on episode boundaries)
- Strategy dispatch in fused_training.rs
Backtest SEGV fix:
- Missing q_gaps_buf argument in action_select kernel launch
- Dynamic branch_sizes from agent (not hardcoded)
Local test: objective=10.48, Sharpe=0.0419, 175K trades, zero errors
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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