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>
The mirror universe feature negated only features[0..3] (4 return-like
features) but inverted the direction action (Short↔Long). The remaining
38 directional features (momentum, trend, patterns) were NOT negated.
This taught the model the WRONG direction on 50% of epochs, cancelling
out the directional signal and producing worse-than-random performance.
Disabled until proper full-feature mirroring is implemented.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replaces broken 9-output exposure branch with two 3-output branches.
Each 3-output branch has -1/2 gradient (33%) — strong enough for dueling.
Eliminates i%3 Q-value degeneracy permanently.
3 novel gems: direction-specific dense reward, per-branch epsilon,
Flat detection shortcut with gradient zeroing.
~480 lines of dead code removed (aux optimizer, CEA, pre-training).
20+ files changed. Full CUDA, no CPU path, no stubs.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Fix 2: Eliminate validation graph re-capture (DtoD into same buffers)
Fix 3: Async validation on separate CUDA stream (overlap with experience)
Fix 4: Aux GEMM in graph_aux (GPU scalar + kernel sig change)
Fix 7: Mega-graph fusion (spectral+forward+aux → 2 launches)
Combined with 5 implemented fixes: 47s → ~16s/epoch
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
5 categories, 18 items:
A) v7.1 fixes: aux GEMM, CEA warmup, OFI gate, dead code
B) Bootstrap trap: GPU cosine epsilon, n-step→5, dense micro-reward
C) Initialization: supervised pre-train, pessimistic Q-init, phase schedule
D) Advanced: TD(λ), PopArt normalization, curriculum learning, hindsight relabel
E) Dead code removal
4 new CUDA kernels, 3 modified, 12 new config fields, 4 removed.
Full GPU, no CPU path, no memory copies. Target: 50%+ win rate.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
bf16 has $32 resolution at $5K equity. Transaction costs ($12.50),
tick PnL ($12.50), and cumulative returns are all below resolution.
All previous hyperopt evaluations used garbage metrics.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Three issues in one spec:
1. H100 hang: cuBLAS set_stream() inside CUDA graph capture → deadlock
2. Bug: train_walk_forward calls non-stratified generate_folds()
3. GPU regime: classification kernel + prefix sum for O(1) range queries
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Documented all findings from the training step performance session:
- Shared memory race was real but not the NaN source (racecheck: 0 hazards)
- NaN is non-deterministic numerical instability, NOT a concurrency bug
- Suspected math bug in gradient budget allocation during MSE warmup
- Investigation plan: GPU NaN detection kernels, gradient audit, git bisect
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Two remaining issues: (1) ~400ms/step instead of ~1-7ms on RTX 3050,
(2) Q-value explosion when C51 kicks in at epoch 2.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Layer 1 now targets experience_action_select (GPU-fused training path)
AND branching_action_select (backtest/fallback), not just the fallback
- Action masking covers both greedy AND random exploration paths
- Exposure index uses parameterized b0_size, not hardcoded 5 or 9
- Fixed end-of-episode variable name (total_bars, not timesteps_per_episode)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause: backtest windows of 300K bars produced ±billions% returns via
multiplicative compounding, and sqrt(252) annualization was wrong for
1-minute bars.
Fixes:
- Window size capped to 10K bars (~25 trading days), evenly distributed
across the full validation set (was clustered in first 6%)
- Annualization: configurable bars_per_day field in GpuBacktestConfig
(default 390.0 for 1-min), produces sqrt(98280) ≈ 313.5
- tanh normalization recalibrated: Sharpe/5, Sortino/8 (was /2, /3)
- CVaR threshold scaled to per-bar: 0.003 with slope 1400 (was 0.05/200)
- VaR/CVaR strided sampling covers full window (was first 4096 only)
- financials.rs + ab_testing.rs: sqrt(252) → sqrt(98280) for consistency
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
RTH/ETH cost differential, market impact (done), book depth fill quality,
macro events, weekend gap risk, margin utilization feature.
All configurable via TOML. We trade against real markets — simulation must match.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
C51 for action selection (expected Q), IQN for risk quantification (CVaR).
Disagreement between C51 and IQN expected Q = uncertainty signal.
Architecture already 90% built — IQN head exists but output unused.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Tests must match reality. All profiles use the full 8-component
composite reward. No legacy PnL-only fallback.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
