Mass deletion of the "v7 gem" reward terms identified in the Phase 1
inventory as category errors — each one rewarded an outcome-adjacent
behavior instead of encoding the underlying physics, and each
empirically hurt validation metrics more than it helped training
stability.
Deleted from experience_kernels.cu and all plumbing (Rust configs,
launch args, hyperopt logs, TOML entries):
* order_credit_weight - reward redundant with compute_tx_cost
(order_type_idx already differentiates
fills by order type)
* risk_efficiency_weight - reward double-counted drawdown penalty
asymmetrically (only on winners)
* urgency_credit_weight - reward was vol-normalized unrealized P&L,
pure rename of core return
* commitment_lambda - triple-counted churn + tx_cost
* w_dsr - kernel wrote DSR EMA but no longer added
to reward (dead); removed the EMA
bookkeeping too
* dsr_eta - kernel arg for the deleted DSR EMA
* position_entropy_weight - rewarded action-bucket diversity
regardless of outcome; histogram buffer
+ zero-init removed too
* exit_timing_weight - already inactive (used raw_next future
price, comment-deleted earlier)
* ofi_reward_weight - dead plumbing; OFI already passed as
feature through state[OFI_START..]
* opportunity_cost_scale - penalized flat when Q-gap wide;
redundant with Q-values themselves
Kernel arg count: experience_env_step_batch shrank from ~55 to ~45 args.
Rust-side config surface reduced correspondingly.
Results on E1 smoke test (20-epoch):
BEFORE any Phase 2 work:
Val Sharpe -120 to -150, MaxDD 10-15%, Sharpe_raw -0.39
AFTER reward_noise + Kelly (both envs) + urgency + this sweep:
Val Sharpe -17 to -22 (7× better)
Val MaxDD 0.27% (40× better)
Val Sharpe_raw ~-0.09 (4× better)
Training Sharpe_raw ~0 (stabilized from ±20 swings)
Final q_gap 0.1712 (highest yet, collapse mechanism fine)
The extreme train-Sharpe swings (+17 one epoch, -13 next) were not
learning dynamics — they were shaping-term noise. Core reward (P&L +
drawdown + churn + holding + tx_cost + Kelly physics cap) gives training
metrics that actually reflect what the model does.
Inventory doc (docs/superpowers/specs/2026-04-21-phase1-reward-inventory.md)
extended with a "better-form taxonomy" section: every deleted gem has
a correct layer it belongs to (physics, feature, diagnostic, gradient-
level regularization — not reward). Kelly cap and Q-target smoothing
are already relocated; others are scheduled per the taxonomy's P1/P2/P3
priority list.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Phase 2 second relocation: the kelly_sizing_weight reward penalty
(experience_kernels.cu:1727-1746 — penalized deviation from Kelly-optimal
sizing) is deleted. Kelly is now a physics constraint in trade_physics.cuh:
the environment refuses to let the agent over-lever, not the reward
scoring the agent for matching a formula.
New helper in trade_physics.cuh (shared device function, reusable by
the forthcoming unified env kernel):
kelly_position_cap(win_count, loss_count, sum_wins, sum_losses,
max_position, safety_multiplier)
Applied in experience_kernels.cu between margin cap and execute_trade,
with health-coupled safety multiplier:
safety = 0.5 + 0.5 × health
- health=1 (healthy): full Kelly — trust the learned policy
- health=0 (collapsing): half Kelly — constrain when decisions less
reliable
Cold-start warmup (critical — otherwise balanced priors yield kelly_f=0
until real trades accumulate, starving Q-learning):
maturity = min(1.0, total_trades / 10)
effective_kelly = maturity × kelly_f + (1 - maturity) × 0.5
Early on (0 trades): cap dominated by 50% floor.
As real trades accumulate (10+): pure data-driven Kelly.
Validation env (backtest_env_kernel.cu) does NOT yet get the Kelly cap —
that requires extending its portfolio state or adding a separate
kelly_stats buffer, which naturally belongs in the Phase 3 unified env
kernel refactor. The current asymmetry is a KNOWN temporary — training
is constrained, validation is not — and will be resolved when both
kernels share the same env_step() device function.
Also completes removal of kelly_sizing_weight from all plumbing:
- experience_kernels.cu: kernel arg deleted
- gpu_experience_collector.rs: launch arg, config field, default
- training_loop.rs: hyperparam propagation
- config.rs: field, default, intensity clamp (with tombstone)
- hyperopt/adapters/dqn.rs: log reference
- config/training/*.toml (6 entries across 4 files): orphan configs
(none were wired to a profile parser field)
Verification:
- cargo check -p ml --lib clean
- E1 smoke test passes: final epoch q_gap=0.1109, health=0.51 (warmup
floor of 0.5 gives early exploration enough room; floor of 0.25
was too tight and failed at q_gap=0.0496)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Phase 2 kick-off from the env-unification design. First relocation: the
reward_noise_scale field that perturbed training rewards is deleted, and
its regularization effect moves to the correct layer — Q-target label
smoothing in c51_loss_kernel.cu — now health-coupled rather than fixed.
Before:
- reward += pseudo_noise × max(|reward| × 0.05, 0.01) (in env reward path)
- Q-target label smoothing = fixed LABEL_SMOOTHING_EPS = 0.01
After:
- Reward untouched by noise. Core reward = actual outcome + aligned penalties.
- Q-target label smoothing eps_eff = 0.02 × (1 − health) read from ISV[12]
- health=1 (healthy): eps_eff=0, sharp targets preserved
- health=0.5: eps_eff=0.01, matches old fixed behavior at mid-health
- health=0 (collapsing): eps_eff=0.02, maximum regularization prevents
overcommitment to the collapsed distribution
Why health-coupled:
Same insight as the distillation SAXPY fix — every fixed kernel scalar is
a temporal-coupling candidate when we have the ISV pinned buffer available.
Regularization strength should scale INVERSELY with network health: it's
most needed exactly when things are falling apart.
Files touched:
- c51_loss_kernel.cu: LABEL_SMOOTHING_EPS const replaced with
LABEL_SMOOTHING_BASE + in-kernel health read from isv_signals[12]
- experience_kernels.cu: deleted reward noise block + kernel arg
- gpu_experience_collector.rs: dropped launch .arg + config field + default
- training_loop.rs: dropped hyperparam propagation
- config.rs: deleted field + intensity clamp + default (with tombstone)
- hyperopt/adapters/dqn.rs: dropped log reference
- config/training/*.toml (4 files): dropped orphan reward_noise_scale
entries (none were being parsed — the profile parser had no field)
Verification:
- `cargo check -p ml --lib` clean
- E1 smoke test passes: final q_gap=0.1190, health=0.52 (health-coupled
smoothing at ~mid-health matches old fixed behavior, collapse-prevention
mechanism intact)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
OFI (Order Flow Imbalance) features are mandatory — the model is worthless
without MBP-10 order book data. Every silent fallback that degraded to zeros
has been converted to a hard error.
Critical fixes:
- build_batch_states used *8 instead of *20 for OFI dimensions — every
walk-forward backtest was reading corrupted OFI features from adjacent memory
- precompute_features early exit skipped cache rebuild when stale v2/v3
cache existed with has_ofi=false — now validates has_ofi before skipping
- 14 silent OFI fallback paths converted to hard errors across data loading,
training loop, experience collector, state construction, metrics, hyperopt
Dead code removed (-751 lines):
- DoubleBufferedLoader (superseded by init_from_fxcache)
- GpuBufferPool (superseded by init_from_fxcache)
- DqnGpuData::upload legacy method (no OFI support)
- CPU training fallback path (CUDA always required)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
target_dim expansion: adds raw_open (OHLCV) and mid_price_open
(MBP-10 midpoint at bar formation) to fxcache targets. FXCACHE_VERSION
2→3 for auto-rebuild. Legacy v2 files handled with close-price fallback.
Spec v5 adds 3 pearls:
- Bar duration encoding in Mamba2 (continuous-time SSM awareness)
- Order book center of mass from all 10 MBP-10 levels (aggression signal)
- Retrospective hold quality bonus (teaches exit timing)
Plus: Hold action (4th direction), DSR Sharpe EMA fix, counterfactual
magnitude/order sign fix, MFT mid-price mark-to-market.
OFI embed MLP now 18→10 (was 16→8). Mamba2 width SH2+10 (was SH2+8).
Attention width D+10 (was D+8).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Removed: enforce_hold(), min_hold_bars from config/kernels/backtest.
Added: holding_cost_rate (inventory penalty), churn_threshold_bars +
churn_penalty_scale (graduated flip penalty) to reward in
experience_env_step and backtest kernels.
The model learns optimal hold timing from cost signals:
- Per-trade tx cost prevents churning (existing)
- Inventory penalty makes large positions expensive to hold
- Churn penalty graduates cost for rapid flips
- Temporal attention learns when holding cost > expected profit
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
fxcache: OFI_DIM=20 (pub const), RECORD_F64_COUNT=66, 272 bytes/bar.
constructor: state_dim 74→86 (aligned 88) with OFI.
experience collector: ofi_dim detection 8→20.
All [f64; 8] → [f64; 20]. Existing 8 features preserved at 0-7.
New 12 features zero-padded until precompute is extended.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
evaluate_dqn_graphed now takes Option<&mut dyn QValueProvider>.
Training eval passes Some(fused_ctx), standalone eval uses closure path.
Removed dead evaluate_dqn non-graphed branch from evaluate_baseline.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace the evaluator's independent CublasForward with QValueProvider
trait that routes Q-value computation through the trainer's graphed
CublasForward. This eliminates the separate cuBLAS handle that caused
evaluation non-determinism.
Architecture:
- QValueProvider trait in q_value_provider.rs (compute_q_values_to)
- FusedTrainingCtx implements it (chunks through trainer batch_size=64)
- GpuDqnTrainer::compute_q_values_graphed captures eval forward in
CUDA Graph (cuBLAS forward + compute_expected_q) on first call
- Pre-captured at deterministic point (right after mega graph)
- evaluate_dqn_graphed takes &mut dyn QValueProvider (mandatory)
Cleanup (-398 lines):
- Deleted evaluator's internal CublasForward + all chunked scratch buffers
- Deleted compute_q_values (non-graphed), flatten_weights_for_cublas,
ensure_cublas_ready, compute_backtest_param_sizes
- Deleted evaluate_dqn (fallback path)
- Removed dead imports and stale comments
Training: fully bit-identical across runs (CUDA Graph replay).
Evaluation: deterministic within process (graph replay), minor
variation across processes (cublasLtMatmul capture non-determinism).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The 4-branch DQN (direction x magnitude) had 3 degenerate variants
(Short25, Flat, Long25) that all mapped to 0.0 target exposure when
direction=Flat, causing 82% Flat collapse. Collapse these into a
single Flat variant, giving 7 levels (ShortSmall/Half/Full, Flat,
LongSmall/Half/Full) and 63 total factored actions (7x3x3).
- ExposureLevel enum: 9 variants -> 7 (add direction/magnitude/from_dir_mag)
- FactoredAction: 81 -> 63 total actions, from_index/to_index updated
- DQN epsilon-greedy: use from_dir_mag() instead of dir*3+mag indexing
- DQN config: num_actions default 9 -> 7
- PPO action space: 45 -> 63 actions, action masking updated
- Signal adapter CUDA kernel: 5-bin -> 7-bin exposure aggregation
- All tests updated for new variant names and index ranges
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Complete bf16 elimination across all crates (ml, ml-core, ml-dqn, ml-ppo,
ml-supervised). Zero half::bf16, __nv_bfloat16, or CudaSlice<half::bf16>
references remain (verified by grep).
CUDA: All 60+ .cu kernels and .cuh headers converted to native float.
- Half-precision intrinsics (__hmul, __hadd, __hdiv) → float operators
- atomicAddBF16 → native atomicAdd(float)
- bf16 wrapper functions → f32 identity passthroughs
Rust: All CudaSlice<half::bf16> → CudaSlice<f32> across 90+ files.
- htod_f32_to_bf16/dtoh_bf16_to_f32 → htod_f32/dtoh_f32 (direct, no conversion)
- Deleted bf16 mirror infrastructure (DuelingWeightSetBf16, alloc_bf16_mirror, etc.)
- Renamed params_bf16→params_flat, d_value_logits_bf16→d_value_logits, etc.
- Fixed .to_f32() sed damage on Decimal::to_f32() and rng.f32()
FxCache: Single f32 disk format (was bf16/f64 dual-version).
- Deleted --bf16 CLI flag from precompute_features
- PVC cache files need regeneration via precompute_features
TF32 tensor cores activated via cublasLtMatmul CUBLAS_COMPUTE_32F — no
explicit TF32 types needed. Storage is pure f32 everywhere.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
H100 baseline (20 epochs) showed gradient collapse at epoch 10: C51
cross-entropy converges its distributional fit before the policy converges,
leaving zero gradient signal. The collapse happened 5 epochs after C51
reached alpha=1.0 (pure C51, zero MSE).
Fix: cap the C51 alpha ramp at c51_alpha_max (default 0.5) so MSE always
contributes (1 - alpha_max) of the primary gradient. MSE loss measures
Q-error directly and only goes to zero when Q-values are correct, not
just when the distribution shape is right.
- c51_alpha_max added to DQNHyperparameters (default 0.5)
- Added to PSO search space as 15th dimension (range [0.3, 0.9])
- Added to TOML profiles: smoketest, production, hyperopt
- Training loop caps alpha ramp at alpha_max instead of 1.0
- All 907 ml tests + 300 ml-core tests + 6 smoke tests pass
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Three fixes:
1. spread_cost removed contract_multiplier ($50) — was computing in dollars
but PnL is in points, making spread 12.5× actual. The model saw every
trade as guaranteed loss → preferred Flat.
2. Mirror universe now negates ALL 17 directional features (returns, MACD,
Bollinger, SMA ratios, regression slope, CUSUM) + flips RSI. Was only
negating 4, teaching wrong direction on 50% of epochs.
3. tx_cost_multiplier updated to 0.18 bps (IBKR ES RT=$4.50/contract).
min_hold_bars=10 for volume bars (~26s at 23 bars/min).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Wire 4th magnitude branch (size=3) into BranchingDuelingQNetwork construction,
DQNAgentType::branch_sizes() return type, training loop diversity metrics,
backtest config in metrics.rs and hyperopt adapter, and DT pre-training config.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Log v7 reward params after apply_family_scaling() so their scaled values are
visible in trial logs. Update loss_shaping_intensity doc comment to reference
v7 params (CEA, order credit, risk efficiency) instead of v6.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The preload's key-based fxcache lookup failed because the cache key
hash depends on file metadata (size+mtime) which differs between
the precompute pod and hyperopt pod (different PVC mount paths).
Fix: load the first .fxcache file from the cache directory directly.
The precompute step generates exactly one fxcache per dataset — no
ambiguity. Eliminates 285s DBN fallback loading.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace stream.synchronize() with cuStreamWaitEvent for cross-stream
visibility of restored weights. The trainer writes best params on its
stream, records a CudaEvent, and the evaluator waits on that event
on ITS stream via cuStreamWaitEvent — purely GPU-side dependency,
zero CPU blocking.
Previous fix (stream.synchronize) was a CPU stall that hid the issue.
This is the proper CUDA approach: inter-stream event dependencies.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
If a trial corrupts GPU state (CUDA_ERROR_ILLEGAL_ADDRESS from extreme
training), the inter-trial sync now drains the error instead of
propagating it — preventing all subsequent trials from failing.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The data preload created a dummy DQNTrainer with buffer_size=1, which
fails the GPU PER capacity check (capacity must be >= batch_size).
This caused the preload to fail silently, falling back to per-trial
data loading from disk — 50 × 5s = 250s wasted.
Fix: set buffer_size = max(batch_size, 1024) so the PER allocation
succeeds. The preload trainer doesn't train — it just loads data into
a shared Arc for all trials.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Three issues fixed:
1. Best checkpoint was loaded via safetensors → Candle VarMap, but the
evaluator now reads from fused_ctx GPU buffers. The VarMap weights
were never used — evaluator always saw last-epoch weights, not best.
2. Added save_best_params/restore_best_params to FusedTrainingCtx:
- save: async DtoD copy params_bf16 → best_params_snapshot
- restore: async DtoD copy best_params_snapshot → params_bf16 + unflatten
Zero sync, zero Candle, zero safetensors roundtrip.
3. Hyperopt evaluation now calls restore_best_gpu_params() instead of
loading safetensors checkpoint. Pure GPU path end-to-end.
Added params_bf16()/params_bf16_mut() accessors to GpuDqnTrainer.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Same bug as the training loop validation: hyperopt's backtest evaluator
read weights from agent.get_q_network_vars() (Candle VarMap) which is
NOT synced during fused CUDA training. All previous hyperopt runs were
evaluating with epoch-0 weights — Sharpe scores were meaningless.
Fix: read directly from fused_ctx.online_dueling_ref() /
online_branching_ref() via new DQNTrainer::fused_online_weights() method.
Falls back to error if fused_ctx not available (required in CUDA builds).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Preloaded fxcache truncated to max_bars from training profile
(1.1M → 5000 bars for smoketest, ~40x faster per trial)
- Consolidated 3 redundant DqnTrainingProfile::load() calls into 2
(VRAM gate reuses base_hp in train_with_params)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Load TOML profile BEFORE VRAM budget gate so hidden_dim/batch_size
reflect the actual profile (smoketest=64 vs production=256)
- Fixes backtest_metrics=None: VRAM gate was pruning all trials using
conservative() defaults (hidden=256, batch=1024) on RTX 3050
- Add test_hyperopt_trial_produces_backtest_metrics regression test
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add training_profile field to DQNTrainer (default: "dqn-production")
- Smoketest uses "dqn-smoketest" profile for RTX 3050 compatible sizes
- DQNTrainer fields (data_source, mbp10_data_dir, trades_data_dir) are
source of truth — TOML profile provides training params only
- Fix relative path resolution in preload_data for mbp10/trades dirs
- Add preload_data() call in hyperopt test (fxcache shared via Arc)
- Buffer size floor of 1024 in hyperopt param application
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Adds `has_ofi: bool` to `FxCacheData` and the fxcache binary header
(stored in `reserved[0]`). Propagates through load_fxcache, write_fxcache,
all callers (precompute_features, train_baseline_rl, hyperopt dqn adapter),
existing roundtrip tests, and adds two new has_ofi-specific roundtrip tests.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Different instruments (ES.FUT vs NQ.FUT) and data source modes (ohlcv vs mbp10)
now produce distinct .fxcache keys, preventing silent overwrites and wrong-bar-type
loads at cache lookup time.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Normalize features once in precompute_features (single source of truth).
NormStats saved alongside .fxcache for inference denormalization.
Removed per-fold NormStats computation from train_baseline_rl, hyperopt
adapter, and smoketests — fxcache is pre-normalized, consumers use as-is.
NOTE: features still show raw price values (max=18000+) because
test_data/futures-baseline contains multiple symbols (ES, NQ, ZN, 6E)
mixed into one dataset. The feature extraction pipeline needs
investigation — log returns between different symbols produce garbage.
This is tracked as a separate task.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Epoch duration self-balances: bigger GPU → bigger auto-scaled batch →
fewer steps per epoch. The manual cap created 7 different values
(0, 8, 64, 100, 200, 300, 2000) across configs/tests/examples, making
behavior inconsistent between environments.
Removed from: DQNHyperparameters, training profiles (smoketest,
localdev, production), CLI args, Argo templates, hyperopt adapter,
all test overrides, supervised example.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
gpu_n_episodes was manually overridden in GPU profiles, training configs,
test files, and hyperopt — all set to 0 or small fixed values that
bypassed the auto-scaling logic, causing a div-by-zero crash in
train_baseline_rl.
Now: single auto-scaling path via optimal_n_episodes() from VRAM/SM
count. No manual override field. Cap at 16384 (consistent with
AutoBatchSizer's 8192 cap pattern). Floor at 32 for small GPUs.
Removed gpu_n_episodes from:
- DQNHyperparameters, PpoHyperparameters structs
- All 4 GPU profiles (rtx3050, h100, a100, default)
- Training profiles (smoketest, localdev)
- ExperienceProfile struct + serde
- Hyperopt adapter
- All test overrides
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Remove 8 enable_* from FeatureConfig (ml-features) and 24 from
DQNHyperparameters (ml). All features are always active — no boolean
toggles, no dead conditional branches, no false impression of optionality.
FeatureConfig reduced to single `phase: FeaturePhase` field.
DQNHyperparameters loses 24 fields, downstream conditionals collapsed.
TOML configs cleaned of all enable_* lines.
16 files changed, -461/+181 lines.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Phase system now controls which family intensities are searchable vs
pinned in the 14D layout. Fast (default) searches ALL 14 dims — phased
search was needed for old 30D space but 14D is within PSO's efficient
range. Full/Reward/Risk phases remain for targeted refinement.
Fix test_phase_fast_bounds to verify all dims searchable in Fast.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Tests referenced old DQNParams fields (learning_rate, batch_size,
ensemble_size, etc.) that were absorbed into family intensity scalars.
Rewrote all affected tests to validate the 14D search space layout,
intensity bounds [0.0, 2.0], and round-trip serialization.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Phase 1: Remove 5 DQNConfig boolean flags (use_soft_updates, use_iqn,
enable_q_value_clipping, use_cvar_action_selection, use_count_bonus).
All features now unconditionally active — no dead toggle branches.
Phase 2: Add spectral decoupling (L2 on Q-value logits, Pezeshki 2021)
and manifold mixup (Beta-sampled distribution interpolation with atomic
barrier sync) directly in C51 CUDA loss kernel. Zero CPU involvement.
Phase 3: Tag Experience transitions with market regime (ADX/CUSUM GPU
classifier kernel). Add regime-biased PER sampling via rejection with
IS weight correction. Decay factor controls cross-regime bleeding.
Phase 4: 6 family intensity scalars for hyperopt (adversarial,
regularization, augmentation, loss shaping, ensemble, causal). Scales
34 generalization params through 6 PSO dimensions instead of 34.
Search space: 24D → 30D (families additive, individual params kept).
20 files changed, +563/-69 lines. Full workspace compiles clean.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Remove dead bf16 kernel handles (scatter_insert_bf16, gather_bf16_rows,
gather_bf16, f32_to_bf16_cast), bf16_slice_to_gpu_tensor_gpu converter,
a16 allocator, and dtod_clone_u16 — all obsoleted by the f32 migration.
Fix unused variables (w_ptrs, concat_dim) and unnecessary mut bindings.
Delete 25 stale hyperopt campaign results from local experiments.
All 1254 tests pass (895 ml + 359 ml-dqn), zero compiler warnings.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
6 test assertions hardcoded 22D search space, now 24D (added w_dd,
dd_threshold). Production profile test expected epochs=100, now 200.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Previously fixed at w_dd=1.0, dd_threshold=0.02. Now PSO can search:
- w_dd: [0.0, 5.0] — drawdown penalty weight (0=disabled, 5=aggressive)
- dd_threshold: [0.01, 0.15] — drawdown % before penalty starts
Search space expanded from 22D to 24D. Backward compatible: vectors
shorter than 24 elements use defaults via bounds check.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Root cause: each trial created a fresh MlDevice (new CudaStream) but
the primary CudaContext was shared. cudaFreeAsync returns memory to
the allocating STREAM's pool, not the context's global heap. New
trial's new stream couldn't reclaim the old stream's freed blocks.
Evidence: Trial 0 leaked 2063MB, Trial 2 leaked 3212MB. Available
RAM dropped 10.4GB→5.2GB over 3 trials. Later trials ran on a
memory-starved system, explaining systematic performance degradation.
Fix: fork a new stream from the shared device instead of creating
a fresh MlDevice. Forked streams share the same context and async
memory pool — free_async blocks are immediately available to the
next trial's allocations.
Also fixed: TRIAL_SUMMARY best_epoch now shows actual best, not
epochs_completed.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
