Update README with comprehensive model documentation including distillation students

README.md

@@ -16,7 +16,7 @@ A multi-model neural rendering pipeline designed for real-time game graphics, tr
 
 The Uruk Neural Renderer is organized into a multi-workstream pipeline, where each workstream trains a specialized model family. A policy-compliant orchestrator manages the 4-stage training lifecycle: **Smoke** (bug-catching), **Calibration** (hyperparameter tuning), **Production** (full training with early stopping), and **Distillation** (teacher-to-student compression).
 
-The flagship **V2-Ultra** model uses a 32-channel input (including a 12-channel G-buffer with material IDs, depth, and normals) and achieves **94.6% material accuracy** — enabling physically-correct lighting decisions based on ground-truth geometry rather than screen-space inference.
+The flagship **V2-Ultra** model uses a 32-channel input (including a 12-channel G-buffer with material IDs, depth, and normals) and achieves **94.6% material accuracy** — enabling physically-correct lighting decisions based on ground-truth geometry rather than screen-space inference. This architecture is designed to exceed DLSS 5 quality by relying on deterministic material accuracy rather than AI hallucinations.
 
 ## Models
 
@@ -54,6 +54,20 @@ Best checkpoints from the policy-compliant orchestrator production runs.
 | `workstreams/ws6_runtime_optimization_v2/ws6_v2_best.pt` | WS6 v2 — Runtime Optimization (rerun) | Completed all 500 epochs | 13.7 MB |
 | `workstreams/ws7_scene_to_world_v2/ws7_v2_best.pt` | WS7 v2 — Scene to World | Graph-based scene understanding | 43.7 MB |
 
+### Distillation Students
+
+Distilled student models compressed from the production teachers for optimal runtime performance.
+
+| File | Description | Size |
+|---|---|---|
+| `distillation/ws2_student_best.pt` | WS2 Learned World Model distilled student (best val_loss: 0.000375) | 21.6 MB |
+| `distillation/ws3_student_best.pt` | WS3 World Authoring distilled student (best val_loss: 0.000073) | 38.4 MB |
+| `distillation/ws4_student_best.pt` | WS4 World Remapper distilled student (best val_loss: 0.049) | 90.6 MB |
+| `distillation/ws5_student_best.pt` | WS5 Cinematic Renderer distilled student (best val_loss: 0.216) | 40.6 MB |
+| `distillation/ws6_student_best.pt` | WS6 Runtime Optimization distilled student (best val_loss: 0.000016) | 1.76 MB |
+
+*(Note: Final epoch checkpoints are also available in the repository as `*_student_final.pt`)*
+
 ### Additional Models
 
 | File | Description | Size |
@@ -63,15 +77,9 @@ Best checkpoints from the policy-compliant orchestrator production runs.
 | `ws8_structure_generator/best_model.pt` | WS8 — Structure Generator | 32.4 MB |
 | `onnx/npc_director.onnx` | NPC Director ONNX export | 5.1 MB |
 
-### Distillation Students
-
-| File | Description | Size |
-|---|---|---|
-| `distillation/ws5_student_best.pt` | WS5 Cinematic Renderer distilled student (best val_loss: 0.236) | 38.7 MB |
-
 ## Training Infrastructure
 
-All models were trained on a single **NVIDIA B200** GPU (180 GB VRAM) on RunPod. The multi-workstream orchestrator managed sequential training across all workstreams with automatic stage transitions, early stopping, and plateau detection.
+All models were trained on a single **NVIDIA B200** GPU (183 GB VRAM) on RunPod. The multi-workstream orchestrator managed sequential training across all workstreams with automatic stage transitions, early stopping, and plateau detection.
 
 ## Usage