Update README.md

README.md

@@ -28,8 +28,8 @@ different modalities:
 
 | Path | Model | Modalities | Role in the notebook |
 |---|---|---|---|
-| `conditioning_and_guidance/joint_model` | Unconditional cross-modal MLP | `coordinates` (continuous, 2D) and `index` (discrete, 4 categories) | Intrinsic guidance (Section 4): conditioning a model that was never trained to be conditional |
-| `conditioning_and_guidance/context_model` | The same MLP plus a label context path, trained with 20% context dropout | `coordinates` only; the corner label is passed as `context_data` instead of as a modality | Context conditioning and classifier-free guidance (Sections 5-7) |
+| `conditioning_and_guidance/joint_model` | Unconditional cross-modal MLP | `coordinates` (continuous, 2D) and `index` (discrete, 4 categories) | Intrinsic guidance (Section 3): conditioning a model that was never trained to be conditional |
+| `conditioning_and_guidance/context_model` | The same MLP plus a label context path, trained with 50% context dropout | `coordinates` only; the corner label is passed as `context_data` instead of as a modality | Context conditioning and classifier-free guidance (Sections 4) |
 
 ### Loading
 
@@ -51,16 +51,3 @@ checkpointer = Checkpointer(
 )
 gen_model = nnx.merge(graphdef, checkpointer.restore_ema(params))
 ```
-
-`restore_ema` reads only `ema_params` and `extra`, and applies the same bias
-correction the training loop uses for evaluation.
-
-## Reproducing
-
-```bash
-uv run python tutorials/scripts/train_conditioning_checkpoints.py
-```
-
-The script mirrors the notebook's model definitions and PRNG chain, so it
-reproduces these exact weights. A checkpoint pins the parameter structure: if a
-notebook's network changes, re-run the script and re-upload.