Add interactive GRPO notebook and update README

- Add grpo_lora-interactive-notebook.ipynb for single-GPU GRPO training
  directly in the workbench
- Include "Test the Trained Model" section with dynamic checkpoint loading
- Update README to document both interactive and distributed execution modes
- Update workbench requirements for interactive mode (8 CPU, 64Gi memory)
- Remove custom reward function appendix from both notebooks (out of scope)

Signed-off-by: Fiona-Waters <fiwaters6@gmail.com>
Co-authored-by: Cursor <cursoragent@cursor.com>

Author: Fiona-Waters

examples/fine-tuning/grpo/README.md

@@ -22,11 +22,16 @@ The ART backend time-shares a single GPU between vLLM (inference) and Unsloth (t
 
 The example uses the [Agent-Ark/Toucan-1.5M](https://huggingface.co/datasets/Agent-Ark/Toucan-1.5M) dataset, which contains tool-calling conversations. The reward function verifies that the model produces syntactically correct tool calls with the expected function name and arguments.
 
-## Execution mode
+## Execution Modes
 
-GRPO runs as a **single-GPU TrainJob** submitted via the Kubeflow SDK. ART is single-GPU by design and manages its own vLLM subprocess internally.
+This example provides two notebooks:
 
-The notebook submits a `TrainJob` from a lightweight workbench, and the training runs on a dedicated GPU pod managed by Kubeflow Trainer.
+| Mode | Notebook | Description |
+|------|----------|-------------|
+| **Interactive** | [`grpo_lora-interactive-notebook.ipynb`](./grpo_lora-interactive-notebook.ipynb) | Runs GRPO training directly on the workbench GPU. Best for exploration, prototyping, and quick iteration. |
+| **Distributed** | [`grpo_lora-kubeflow-trainjob.ipynb`](./grpo_lora-kubeflow-trainjob.ipynb) | Submits a Kubeflow TrainJob from a lightweight workbench. Training runs on a dedicated GPU pod. Best for production workloads. |
+
+ART is single-GPU by design and manages its own vLLM subprocess internally.
 
 To learn more about execution modes for other algorithms, see the [fine-tuning execution modes overview](../README.md#execution-modes).
 
@@ -52,13 +57,13 @@ to seamlessly run fine-tuning jobs.
 
 | Image Type | Use Case | GPU | CPU | Memory |
 |------------|----------|-----|-----|--------|
-| Training \| Jupyter \| PyTorch \| CPU Python | Job submission and monitoring | None | 2 cores | 8Gi |
-| Training \| Jupyter \| PyTorch \| CUDA \| Python | Job submission + model evaluation | 1× GPU | 2 cores | 8Gi |
+| Training \| Jupyter \| PyTorch \| CPU Python | Distributed mode: job submission and monitoring | None | 2 cores | 8Gi |
+| Training \| Jupyter \| PyTorch \| CUDA \| Python | Interactive mode, or distributed + model evaluation | 1× GPU (40GB+ VRAM) | 8 cores | 64Gi |
 
 > [!NOTE]
 >
-> - The workbench does not run the training itself — it submits a TrainJob and monitors progress.
-> - A GPU on the workbench is only needed if you want to load and test the fine-tuned LoRA adapter after training completes.
+> - **Distributed mode**: The workbench submits a TrainJob and monitors progress. A GPU on the workbench is only needed to test the fine-tuned LoRA adapter after training completes.
+> - **Interactive mode**: Training runs directly on the workbench GPU. The workbench needs an A100, H100, or L40S (40GB+ VRAM) with sufficient CPU and memory.
 
 ### Training Pod Requirements
 
@@ -135,10 +140,12 @@ to seamlessly run fine-tuning jobs.
 
 ![](../images/05.png)
 
-### Running the example notebook
+### Running the example notebooks
 
 - From the workbench, clone this repository: `https://github.com/red-hat-data-services/red-hat-ai-examples.git`
-- Navigate to the `examples/fine-tuning/grpo` directory and open the [`grpo_lora-kubeflow-trainjob.ipynb`](./grpo_lora-kubeflow-trainjob.ipynb) notebook.
+- Navigate to the `examples/fine-tuning/grpo` directory and open the notebook for your preferred execution mode:
+  - **Interactive**: [`grpo_lora-interactive-notebook.ipynb`](./grpo_lora-interactive-notebook.ipynb) — runs training directly on the workbench GPU
+  - **Distributed**: [`grpo_lora-kubeflow-trainjob.ipynb`](./grpo_lora-kubeflow-trainjob.ipynb) — submits a TrainJob via Kubeflow Trainer
 
 > [!NOTE]
 >