feat(pi0): add train_expert_only and freeze_vision_encoder flags to pi0 and pi0.5 (#2727)

* feat(pi0): add train_expert_only and freeze_vision_encoder options

* pi_05: train_expert_only and freeze_vision_encoder flags

* comment clean up

* docs: add finetuning parameters to pi0 and pi05 docs

* updating docs to follow standards

docs/source/pi05.mdx

@@ -67,6 +67,8 @@ python src/lerobot/scripts/lerobot_train.py\
     --policy.gradient_checkpointing=true \
     --wandb.enable=true \
     --policy.dtype=bfloat16 \
+    --policy.freeze_vision_encoder=false \
+    --policy.train_expert_only=false \
     --steps=3000 \
     --policy.device=cuda \
     --batch_size=32
@@ -82,6 +84,15 @@ python src/lerobot/scripts/lerobot_train.py\
   - [lerobot/pi05_base](https://huggingface.co/lerobot/pi05_base)
   - [lerobot/pi05_libero](https://huggingface.co/lerobot/pi05_libero) (specifically trained on the Libero dataset)
 
+### Training Parameters Explained
+
+| Parameter               | Default | Description                                 |
+| ----------------------- | ------- | ------------------------------------------- |
+| `freeze_vision_encoder` | `false` | Do not freeze the vision encoder            |
+| `train_expert_only`     | `false` | Do not freeze the VLM, train all parameters |
+
+**💡 Tip**: Setting `train_expert_only=true` freezes the VLM and trains only the action expert and projections, allowing finetuning with reduced memory usage.
+
 If your dataset is not converted with `quantiles`, you can convert it with the following command:
 
 ```bash