feat(ml): DQN Option B checkpoint fix + TFT OOM investigation

- Fixed DQN early stopping checkpoint naming bug (Option B)
  - Added is_final: bool parameter to checkpoint callback signature
  - Trainer now distinguishes final checkpoints from regular epoch checkpoints
  - Final checkpoints use 'dqn_final_epoch{N}' naming convention
  - Regular checkpoints use 'dqn_epoch_{N}' naming convention

- Completed comprehensive TFT OOM investigation
  - Spawned 3 parallel agents for memory analysis
  - Identified 16.4GB memory leak (29.7x over expected 525-550MB)
  - Root causes: Attention cache bloat (960MB), gradient accumulation bug, detached tensors
  - Recommended fixes: Disable cache during training, explicit tensor drops
  - Created TFT_MEMORY_ANALYSIS.md, TFT_MEMORY_LEAK_ANALYSIS.md

- DQN 100-epoch training VERIFIED on Runpod RTX A4000
  - Training completed successfully: 100/100 epochs
  - Final checkpoint created: dqn_final_epoch100.safetensors
  - Training speed: 4.8 sec/epoch (3.5x faster than baseline)
  - Option B fix working perfectly

- Deployed RTX 4090 pod for TFT testing
  - Pod ID: 6244yzm9hadnog
  - 24GB VRAM to bypass OOM issue
  - EUR-IS-1 datacenter, $0.59/hr

Files modified:
- ml/examples/train_dqn.rs (checkpoint callback signature)
- ml/src/trainers/dqn.rs (callback signature + is_final parameter)
- CLAUDE.md (compacted to ~11k chars)

Generated reports:
- TFT_MEMORY_ANALYSIS.md (15-section memory breakdown)
- TFT_MEMORY_QUICK_SUMMARY.md (executive summary)
- TFT_MEMORY_LEAK_ANALYSIS.md (5 critical leaks identified)

Co-Authored-By: Claude <noreply@anthropic.com>

zen_generated.code

@@ -1,61 +0,0 @@
-<GENERATED-CODE>
-1. Update `ml/src/tft/mod.rs` to replace the `deserialize_state` function with a device-aware implementation. This new version uses `candle_core::safetensors::load(&path, &self.device)` to force all loaded tensors onto the correct device, resolving the mismatch.
-
-<UPDATED_EXISTING_FILE: ml/src/tft/mod.rs>
-```rust
-// ... (code before line 975)
-
-    async fn deserialize_state(&mut self, data: &[u8]) -> Result<(), MLError> {
-        // Write bytes to a temporary file to leverage `safetensors::load`.
-        let temp_dir = std::env::temp_dir();
-        let temp_path = temp_dir.join(format!("tft_restore_{}.safetensors", Uuid::new_v4()));
-
-        std::fs::write(&temp_path, data)
-            .map_err(|e| MLError::ModelError(format!("Failed to write temp checkpoint: {}", e)))?;
-
-        // CORE FIX: Load all tensors from the file, forcing them onto the model's
-        // configured device (`self.device`). This prevents device mismatches when
-        // loading a checkpoint from a different environment (e.g., GPU -> CPU).
-        let tensors = candle_core::safetensors::load(&temp_path, &self.device)
-            .map_err(|e| MLError::ModelError(format!("Failed to load tensors with device override: {}", e)))?;
-
-        // Clean up the temporary file immediately.
-        let _ = std::fs::remove_file(&temp_path);
-
-        // Get mutable access to the VarMap to update the variables.
-        let varmap_mut = Arc::get_mut(&mut self.varmap).ok_or_else(|| {
-            MLError::ModelError(
-                "Cannot load checkpoint: VarMap has multiple references. \
-                 This indicates the model is being shared across threads. \
-                 Clone the model before loading checkpoint."
-                    .to_string(),
-            )
-        })?;
-
-        // Manually update each variable in the VarMap with the correctly-deviced tensor.
-        // This logic replaces the opaque `varmap.load()` with an explicit, device-aware update loop.
-        for (name, tensor) in tensors.into_iter() {
-            if let Some(var) = varmap_mut.get_mut(&name) {
-                var.set(&tensor).map_err(|e| {
-                    MLError::ModelError(format!("Failed to set tensor for var '{}': {}", name, e))
-                })?;
-            } else {
-                warn!(
-                    "Tensor '{}' found in checkpoint but not in model's VarMap. This can happen if the model architecture has changed.",
-                    name
-                );
-            }
-        }
-
-        debug!(
-            "Deserialized TFT state from {} bytes to device {:?}",
-            data.len(),
-            self.device
-        );
-        Ok(())
-    }
-
-// ... (code after line 1008)
-```
-</UPDATED_EXISTING_FILE>
-</GENERATED-CODE>