docs: Small update to sft documentation

docs/guides/sft.md

@@ -6,9 +6,9 @@ The script [examples/run_sft.py](../../examples/run_sft.py) can be used to launc
 
 Be sure to launch the job using `uv`. The command to launch an SFT job is as follows:
 ```bash
-uv run examples/run_sft.py --config <PATH TO YAML CONFIG> --output-dir <PATH TO OUTPUT DIRECTORY>
+uv run examples/run_sft.py --config <PATH TO YAML CONFIG>
 ```
-If not specified, `config` will default to [examples/configs/sft.yaml](../../examples/configs/sft.yaml) and `output-dir` will default to `./outputs`.
+If not specified, `config` will default to [examples/configs/sft.yaml](../../examples/configs/sft.yaml).
 
 ## Configuration
 
@@ -17,16 +17,16 @@ Reinforcer allows users to configure experiments using `yaml` config files. An e
 To override a value in the config, either update the value in the `yaml` file directly, or pass the override via the command line. For example:
 
 ```bash
-python examples/run_sft.py \
-    data.max_input_seq_length=8192 \
-    logger.wandb.name="sft-dev-sl-8192"
+uv run examples/run_sft.py \
+    cluster.gpus_per_node=1 \
+    logger.wandb.name="sft-dev-1-gpu"
 ```
 
 ## Datasets
 
 SFT datasets in Reinforcer are encapsulated using classes. Each SFT data class is expected to have the following attributes:
-  - `formatted_ds`: The dictionary of formatted datasets. This dictionary should contain `train` and `validation` splits, and each split should conform to the format described below.
-  - `task_spec`: The `TaskDataSpec` for this dataset. This should specify the name you choose for this dataset as well as the `custom_template` for this dataset. More on custom templates below.
+  1. `formatted_ds`: The dictionary of formatted datasets. This dictionary should contain `train` and `validation` splits, and each split should conform to the format described below.
+  2. `task_spec`: The `TaskDataSpec` for this dataset. This should specify the name you choose for this dataset as well as the `custom_template` for this dataset. More on custom templates below.
 
 SFT datasets are expected to follow the HuggingFace chat format. Refer to the [chat dataset document](../design_docs/chat_datasets.md) for details. If your data is not in the correct format, simply write a preprocessing script to convert the data into this format. [data/hf_datasets/squad.py](../../nemo_reinforcer/data/hf_datasets/squad.py) has an example:
 
@@ -62,4 +62,7 @@ task_spec = TaskDataSpec(
 )
 ```
 
-By default, NeMo-Reinforcer has support for `Squad` and `OpenAssistant` datasets. If you would like to use a custom dataset, create a new dataset class with the expected attributes.
+By default, NeMo-Reinforcer has support for `Squad` and `OpenAssistant` datasets. Both of these datasets are downloaded from HuggingFace and preprocessed on-the-fly, so there's no need to provide a path to any datasets on disk.
+
+Adding a new dataset is a straightforward process.
+As long as your custom dataset has the `formatted_ds` and `task_spec` attributes described above, it can serve as a drop-in replacement for Squad and OpenAssistant.