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+# Deterministic Forward via Training Graph Replay
+
+## Problem
+
+The backtest evaluator produces different val_Sharpe across process invocations despite identical training weights. Root cause: cublasLtMatmul in separate CUDA Graph capture sessions produces different internal kernel configurations.
+
+## Solution
+
+Replay the training `graph_forward` for validation. Same captured graph, same kernels, bit-identical results.
+
+## Design
+
+### Forward Pass for Validation
+
+1. Write states into `trainer.states_buf` via `launch_pad_states`
+2. Replay `graph_forward` (cuBLAS forward + C51 loss + C51 grad + cuBLAS backward)
+3. Launch `compute_expected_q` kernel on the logits
+4. Read Q-values from `q_out_buf`
+
+Loss/grad/backward writes go to scratch buffers overwritten on next training step. Harmless.
+
+### Changes
+
+**gpu_dqn_trainer.rs:**
+- Delete `compute_q_values_graphed` and `eval_fwd_graph` field
+- Add `replay_forward_for_q_values(batch_size)`: replays graph_forward + expected_q
+- Keep `graph_forward` alive even in mega-graph mode (currently set to None)
+
+**fused_training.rs:**
+- QValueProvider calls `replay_forward_for_q_values` instead of `compute_q_values_graphed`
+- Delete pre-capture logic from mega graph capture
+
+### Constraints
+
+- `graph_forward` must remain captured (not cleared when mega graph supersedes)
+- Batch size: QValueProvider chunks through trainer batch_size
+- v_range: pinned device-mapped pointer (graph reads updated value on replay)
+
+### Performance
+
+~0.3ms extra per batch from wasted loss/grad/backward. Once per epoch. Negligible.
+
+### Success Criteria
+
+4 consecutive test runs produce identical val_Sharpe across all 30 epochs.