Single cuGraphExecLaunch per step. PER sampling, gather, training,
priority update ALL as child graph nodes in one parent. Direct-to-trainer
gather eliminates DtoD copies. GPU-side counters eliminate host writes.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause: CUfunction sharing between graphed children and ungraphed
outside-graph ops corrupts kernel state on Hopper → 3100ms adam replay.
Fix: capture EVERYTHING unconditionally in child graphs. Selectivity,
denoise, causal intervention, vaccine, Q-stats all become graphed children.
Zero ungraphed launches = zero CUfunction conflicts.
7 children, ~190 kernel nodes, one capture, one replay per step.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Phase 1 results: 4.0s/step GPU compute (graph replay = ungraphed, cuBLAS
replaced manual matmul but feature count increased). CPU pipeline fully
async (0.1ms/step). Total ~718s/epoch — 100% GPU bound.
Updated Phase 2 targets: aux parallelism (2b) is highest priority —
80/177 kernels with 4 independent trainers. nsys profiling should run
first to validate SM vs memory-bound assumptions.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Inventory penalty makes flat optimal — model learns to do nothing.
Instead: penalize inaction proportional to model's own predicted edge
(Q-value spread). Creates virtuous cycle: better temporal attention →
higher self-imposed penalty for missed trades → more trading on signal.
No hindsight bias (uses predicted edge, not actual price change).
Micro-reward (already exists) rewards correct positioning.
Churn penalty (new) prevents rapid flips.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
forward_child: 4 branch streams fork after h_s2, join before loss
aux_child: IQL/IQN/attention on parallel streams
Uses CU_STREAM_CAPTURE_MODE_RELAXED with fork-join events as graph edges
Existing branch_streams[4] + events in batched_forward.rs ready to activate
Target: <40s epochs (from <80s Phase 1)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Portfolio slots ps[15]-ps[19] are Kelly accumulators (active).
Plan params moved to ps[23]-ps[29]. PORTFOLIO_STRIDE grows 23→30.
Fixed asymmetry: scales profit_target only (not stop_loss).
Clarified epsilon mid-plan: overridden by direction lock (intended).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Components were fighting a broken signal, not broken themselves.
With correct signal: adapt DSR to trade-level, rank only non-zero
rewards, keep commitment as soft signal, let E1 enrichment handle
Q-drift instead of hardcoded kernel penalty.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause: training environment differs from validation backtest in 3 ways:
1. 100-bar episodes force exits (val runs continuous) → position-gated done
2. Rank normalization destroys sparse trade-level reward → remove it
3. Different Sharpe computation (per-trade vs per-bar, different annualization)
Single execution path:
Phase 1: Episode alignment (100→5000 bars, position-gated done, soft reset)
Phase 2: Reward alignment (remove rank norm, raw trade P&L to replay)
Phase 3: Metrics alignment (un-annualized per-trade Sharpe, both paths)
Expected: training Sharpe within 2× of validation with same weights.
Removes 5 unnecessary shaping components that fought the broken signal.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Every epoch's validation backtest feeds back into the next epoch:
E1: Q-value reality check (bias correction, replaces drift penalty)
E2: Adaptive epsilon (performance-driven, replaces schedule)
E3: Dynamic gamma (from trade duration, replaces manual annealing)
E4: Trade autopsy (per-branch LR scaling from error rates)
E5: Ensemble agreement tuning (auto-tune epistemic gate)
E6: Winner distillation (boost top 10% trades in replay)
E7: Hindsight optimal labels (correct actions for losers)
E8: Curriculum weights (oversample failure regimes)
E9: State confidence (tradability scores from eval)
Zero hardcoded schedules. The model adapts from its own performance.
~215 lines Rust, no new CUDA kernels. ~15s overhead per epoch.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Two fundamental fixes for training Sharpe breakthrough:
1. Trade-level rewards: replace per-bar noise (SNR=0.01) with trade
P&L attribution (SNR=0.1-0.5). C51 atoms model trade outcome
distributions, not random walk noise.
2. Exploration risk budget: protection stack (CVaR, epistemic gate,
commitment, DSR) scaled by iqn_readiness². Loose during exploration,
tight when converged. Model can discover edges before being punished.
Also: homeostatic regularization spec (unified adaptive penalties).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Primary goal: val/OOS Sharpe gap < 15%. If val_Sharpe drops to 25
post-generalization, OOS should be > 21. If val drops to 15, OOS > 13.
OOS Sharpe > 10 sustained as secondary target.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Walk-forward: strictly chronological, no future leakage.
Weight decay mask: indices 0-7 (trunk + value head), not just trunk.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Observed train-vpb4w freeze at epoch 23 (val_Sharpe=51.11):
- avg_grad=10.0 (nonzero!) but Q-stats FROZEN for 4+ epochs
- Root cause: Adam momentum converged to fixed point, not zero gradient
Component 10: Targeted Adam momentum reset for branch weights only
(indices 8-41). Lighter than full rewind, preserves trunk convergence.
Component 11: Q-gap target attractor for spread gradient. Amplifies
spread when Q-gap is below target, reduces when above. Prevents
spread from being too weak at high Q-gap levels.
Component 12: Cosine LR schedule with warm restarts (T=20 epochs).
LR decay prevents overshoot, restart jumps break fixed points.
Half-momentum at restart preserves gradient direction.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Observed in train-vpb4w (live H100 run):
- Q-mean drifts 0→+0.47 over 18 epochs (bootstrapping bias)
- Atom utilization drops 100%→20% (distributional collapse)
- val_Sharpe peaks 54.59 then decays to 31.52
Component 8: Mean-center Q-values before action selection pipeline.
Zero params, preserves ranking, prevents drift accumulation.
Component 9: Adaptive atom entropy regularization. Ramps entropy_coeff
when utilization drops below 50%. Uses existing c51_grad_kernel param.
Both are zero-param fixes moved to top of implementation order.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Checkpoint ring buffer ranked by improvement rate, plateau detection
via liquid tau velocity, informed perturbation cycle (Adam reset,
shrink-perturb, temp boost, LR×2), depth-limited rewind. ~150 lines
in training_loop.rs, zero CUDA changes. Recovers from plateaus that
prevention mechanisms (spread gradient, liquid tau) fail to avoid.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Magnitude FC input changes from h_s2[B, SH2] to [h_s2; Q_dir][B, SH2+3].
Direction E[Q] values (3 scalars per sample) are computed from branch 0
logits and concatenated. w_b1fc grows by 3*adv_h parameters (768 for AH=256).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Covers: PER advantage-weighted staleness, per-sample C51 atom
support centered on V(s), per-branch advantage decomposition,
dual-tau expectile gap exploration, and v_range dead code removal.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
VarStore is a CPU-trip corrupting data + degrading performance. Must be
replaced with direct pointer buffers (flat params_buf is already the
source of truth). Checkpoint, weight extraction, and polyak updates
all need porting to direct CudaSlice operations.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
10-component design to fix 82% Flat/Small action concentration:
1. Collapse degenerate 9→7 level exposure space
2. Per-branch reward decomposition (direction/magnitude/order/urgency)
3. Hindsight action relabeling for magnitude
4. Position-aware temporal exploration with per-branch epsilon
5. IQN quantile spread as anti-collapse signal
6. Activate PopArt + couple with tau reset
7. Feed the gradient vaccine with diverse batches
8. Per-branch bottleneck widths
9. Ensemble variance at inference
10. Clear PER between folds
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Single train template replaces 7 overlapping workflows. Binary cache
on PVC by commit SHA, fxcache skip-if-exists. Delete 5 dead templates.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
C51 cross-entropy structurally prefers low-variance actions (Small positions
have tighter return distributions). MSE is variance-neutral. By reducing
C51's gradient to 20% and amplifying MSE to 4× for the magnitude branch,
MSE becomes the dominant loss signal for position sizing.
Direction/order/urgency keep full C51 gradient for distributional risk awareness.
Replaces the previous mag_amp=1.5 which addressed a symptom (Flat gradient
starvation) rather than the root cause (C51 variance bias).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
