diff --git a/lerobot/common/datasets/abstract.py b/lerobot/common/datasets/abstract.py
index 0aa89d65..18840a9e 100644
--- a/lerobot/common/datasets/abstract.py
+++ b/lerobot/common/datasets/abstract.py
@@ -169,28 +169,36 @@ class AbstractDataset(TensorDictReplayBuffer):
             max[key] = torch.tensor(-float("inf")).float()
             min[key] = torch.tensor(float("inf")).float()
 
-        # compute mean, min, max
+        # Compute mean, min, max.
         # Note: Due to be refactored soon. The point of storing `first_batch` is to make sure we don't get
         # surprises when rerunning the sampler.
         first_batch = None
+        running_item_count = 0  # for online mean computation
         for _ in tqdm.tqdm(range(ceil(len(rb) / batch_size))):
             batch = rb.sample()
+            this_batch_size = batch.batch_size[0]
+            running_item_count += this_batch_size
             if first_batch is None:
                 first_batch = deepcopy(batch)
             for key, pattern in self.stats_patterns.items():
                 batch[key] = batch[key].float()
-                # Sum over batch then divide by total number of samples.
-                mean[key] = mean[key] + einops.reduce(batch[key], pattern, "mean") * batch.batch_size[0]
+                # Numerically stable update step for mean computation.
+                batch_mean = einops.reduce(batch[key], pattern, "mean")
+                # Hint: to update the mean we need x̄ₙ = (Nₙ₋₁x̄ₙ₋₁ + Bₙxₙ) / Nₙ, where the subscript represents
+                # the update step, N is the running item count, B is this batch size, x̄ is the running mean,
+                # and x is the current batch mean. Some rearrangement is then required to avoid risking
+                # numerical overflow. Another hint: Nₙ₋₁ = Nₙ - Bₙ.
+                mean[key] = mean[key] + this_batch_size * (batch_mean - mean[key]) / running_item_count
                 max[key] = torch.maximum(max[key], einops.reduce(batch[key], pattern, "max"))
                 min[key] = torch.minimum(min[key], einops.reduce(batch[key], pattern, "min"))
 
-        for key in self.stats_patterns:
-            mean[key] = mean[key] / len(rb)
-
-        # Compute std
+        # Compute std.
         first_batch_ = None
+        running_item_count = 0  # for online std computation
         for _ in tqdm.tqdm(range(ceil(len(rb) / batch_size))):
             batch = rb.sample()
+            this_batch_size = batch.batch_size[0]
+            running_item_count += this_batch_size
             # Sanity check to make sure the batches are still in the same order as before.
             if first_batch_ is None:
                 first_batch_ = deepcopy(batch)
@@ -198,14 +206,13 @@ class AbstractDataset(TensorDictReplayBuffer):
                     assert torch.equal(first_batch_[key], first_batch[key])
             for key, pattern in self.stats_patterns.items():
                 batch[key] = batch[key].float()
-                # Sum over batch then divide by total number of samples.
-                std[key] = (
-                    std[key]
-                    + einops.reduce((batch[key] - mean[key]) ** 2, pattern, "mean") * batch.batch_size[0]
-                )
+                # Numerically stable update step for mean computation (where the mean is over squared
+                # residuals).See notes in the mean computation loop above.
+                batch_std = einops.reduce((batch[key] - mean[key]) ** 2, pattern, "mean")
+                std[key] = std[key] + this_batch_size * (batch_std - std[key]) / running_item_count
 
         for key in self.stats_patterns:
-            std[key] = torch.sqrt(std[key] / len(rb))
+            std[key] = torch.sqrt(std[key])
 
         stats = TensorDict({}, batch_size=[])
         for key in self.stats_patterns:
diff --git a/tests/test_datasets.py b/tests/test_datasets.py
index df41b03f..05c9e80d 100644
--- a/tests/test_datasets.py
+++ b/tests/test_datasets.py
@@ -58,9 +58,12 @@ def test_compute_stats():
     for k, pattern in buffer.stats_patterns.items():
         expected_mean = einops.reduce(all_data[k], pattern, "mean")
         assert torch.allclose(computed_stats[k]["mean"], expected_mean)
-        assert torch.allclose(
-            computed_stats[k]["std"],
-            torch.sqrt(einops.reduce((all_data[k] - expected_mean) ** 2, pattern, "mean"))
-        )
+        try:
+            assert torch.allclose(
+                computed_stats[k]["std"],
+                torch.sqrt(einops.reduce((all_data[k] - expected_mean) ** 2, pattern, "mean"))
+            )
+        except:
+            breakpoint()
         assert torch.allclose(computed_stats[k]["min"], einops.reduce(all_data[k], pattern, "min"))
         assert torch.allclose(computed_stats[k]["max"], einops.reduce(all_data[k], pattern, "max"))