diff --git a/README.md b/README.md
index 96d51ee1..f7165573 100644
--- a/README.md
+++ b/README.md
@@ -32,6 +32,12 @@ The following command will evaluate the probe from the run naughty-northcutt on
 elk eval naughty-northcutt microsoft/deberta-v2-xxlarge-mnli imdb
 ```
 
+The following runs `elicit` on the Cartesian product of the listed models and datasets, storing it in a special folder ELK_DIR/sweeps/<memorable_name>. Moreover, `--add_pooled` adds an additional dataset that pools all of the datasets together.
+
+```bash
+elk sweep --models gpt2-{medium,large,xl} --datasets imdb amazon_polarity --add_pooled
+```
+
 ## Caching
 
 The hidden states resulting from `elk elicit` are cached as a HuggingFace dataset to avoid having to recompute them every time we want to train a probe. The cache is stored in the same place as all other HuggingFace datasets, which is usually `~/.cache/huggingface/datasets`.
diff --git a/elk/__main__.py b/elk/__main__.py
index faa044f7..a477e799 100644
--- a/elk/__main__.py
+++ b/elk/__main__.py
@@ -5,6 +5,7 @@
 from simple_parsing import ArgumentParser
 
 from elk.evaluation.evaluate import Eval
+from elk.training.sweep import Sweep
 from elk.training.train import Elicit
 
 
@@ -12,14 +13,14 @@
 class Command:
     """Some top-level command"""
 
-    command: Elicit | Eval
+    command: Elicit | Eval | Sweep
 
     def execute(self):
         return self.command.execute()
 
 
 def run():
-    parser = ArgumentParser(add_help=False, add_config_path_arg=True)
+    parser = ArgumentParser(add_help=False)
     parser.add_arguments(Command, dest="run")
     args = parser.parse_args()
     run: Command = args.run
diff --git a/elk/training/sweep.py b/elk/training/sweep.py
new file mode 100644
index 00000000..7f2bef2a
--- /dev/null
+++ b/elk/training/sweep.py
@@ -0,0 +1,60 @@
+from dataclasses import InitVar, dataclass
+
+from ..extraction import Extract, PromptConfig
+from ..files import elk_reporter_dir, memorably_named_dir
+from .train import Elicit
+
+
+@dataclass
+class Sweep:
+    models: list[str]
+    """List of Huggingface model strings to sweep over."""
+    datasets: list[str]
+    """List of dataset strings to sweep over. Each dataset string can contain
+    multiple datasets, separated by plus signs. For example, "sst2+imdb" will
+    pool SST-2 and IMDB together."""
+    add_pooled: InitVar[bool] = False
+    """Whether to add a dataset that pools all of the other datasets together."""
+
+    name: str | None = None
+
+    def __post_init__(self, add_pooled: bool):
+        if not self.datasets:
+            raise ValueError("No datasets specified")
+        if not self.models:
+            raise ValueError("No models specified")
+
+        # Add an additional dataset that pools all of the datasets together.
+        if add_pooled:
+            self.datasets.append("+".join(self.datasets))
+
+    def execute(self):
+        M, D = len(self.models), len(self.datasets)
+        print(f"Starting sweep over {M} models and {D} datasets ({M * D} runs)")
+        print(f"Models: {self.models}")
+        print(f"Datasets: {self.datasets}")
+
+        root_dir = elk_reporter_dir() / "sweeps"
+        sweep_dir = root_dir / self.name if self.name else memorably_named_dir(root_dir)
+        print(f"Saving sweep results to \033[1m{sweep_dir}\033[0m")  # bold
+
+        for i, model_str in enumerate(self.models):
+            # Magenta color for the model name
+            print(f"\n\033[35m===== {model_str} ({i + 1} of {M}) =====\033[0m")
+
+            for dataset_str in self.datasets:
+                out_dir = sweep_dir / model_str / dataset_str
+
+                # Allow for multiple datasets to be specified in a single string with
+                # plus signs. This means we can pool datasets together inside of a
+                # single sweep.
+                datasets = [ds.strip() for ds in dataset_str.split("+")]
+                Elicit(
+                    data=Extract(
+                        model=model_str,
+                        prompts=PromptConfig(
+                            datasets=datasets,
+                        ),
+                    ),
+                    out_dir=out_dir,
+                ).execute()