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+# DQN Generalization Gap — Action Plan
+
+## Problem
+In-sample Sharpe +0.57, OOS Sharpe -1.30 on H100. Model performs worse than random OOS.
+100K+ IS trades vs 211 OOS trades — model learned IS-specific trading patterns.
+
+## Root Causes (from codebase analysis)
+1. **Deterministic episodes** — same start points every epoch, enables memorization
+2. **CQL alpha=0.1** — effectively disabled (1.5% of gradient budget)
+3. **Fixed simulation params** — spread=1bp, tx_cost=1.0, fill_prob=0.85 never vary
+4. **Single-fold validation** — 80/20 time split, no purging/embargo
+5. **No data augmentation** — pixel-perfect price sequences
+
+## Execution Order
+
+### Phase 1: Quick Wins (1-2 days)
+
+**1a. Domain Randomization (Rust only, per-epoch)**
+- `training_loop.rs` lines 900-990: randomize before kernel launch
+- spread: U[0.5bp, 3.0bp], tx_cost: U[0.5, 2.5], fill probs: randomized
+- Episode starts: add ±25% stride jitter (line 935)
+- Variable episode length: U[250, 750] timesteps
+
+**1b. CQL Alpha + Gradient Budget**
+- `config.rs` line 1509: `cql_alpha: 0.1 → 1.0`
+- `fused_training.rs` line 51: `CQL_GRAD_BUDGET: 0.15 → 0.25`
+- C51 budget: 0.70 → 0.60
+- Add `cql_alpha` warmup synchronized with c51_warmup
+
+### Phase 2: Walk-Forward (3-5 days)
+
+**2a. Enable GPU walk-forward by default**
+- `config.rs`: `enable_gpu_walk_forward: true`
+- Configure 6-8 folds with purge gap = n_steps bars
+- Embargo gap = 100 bars (DSR EMA window)
+- Model selection: median OOS Sharpe across folds
+
+### Phase 3: Multi-Instrument (2-3 weeks)
+
+**3a. ES + NQ dual training**
+- Shared trunk, instrument-specific advantage heads
+- Normalized rewards (vol-adjusted, instrument-agnostic)
+- Data loading for multiple .dbn.zst files
+
+## Expected Impact
+- Phase 1: 50-70% generalization gap reduction
+- Phase 2: 30-50% PBO reduction
+- Phase 3: 8-12% OOS improvement