diff --git a/code/tests/test_training_utils.py b/code/tests/test_training_utils.py
index 4d9c68e..b6b5f8b 100644
--- a/code/tests/test_training_utils.py
+++ b/code/tests/test_training_utils.py
@@ -128,3 +128,38 @@ def test_non_tensor_passthrough(self):
         out = dict_to_device(d, torch.device("cpu"))
         self.assertEqual(out["a"], 1)
         self.assertEqual(out["b"], "x")
+
+
+class TestStepsPerEpochCalculation(unittest.TestCase):
+    """steps_per_epoch must count forward passes (one generate_batch per step),
+    not optimizer steps.  accumulate_steps only controls optimizer.step()
+    frequency and must not reduce the loop iteration count."""
+
+    def _steps_per_epoch(self, num_samples, per_device_bs, world_size):
+        """Mirror the calculation in train.py:main() (line ~1298)."""
+        return num_samples // (per_device_bs * world_size)
+
+    def test_accumulate_steps_does_not_reduce_data_seen(self):
+        num_samples = 8_388_608
+        batch_size = 512
+        world_size = 1
+
+        steps = self._steps_per_epoch(num_samples, batch_size, world_size)
+        data_seen = steps * batch_size * world_size
+
+        self.assertEqual(data_seen, num_samples)
+
+    def test_multi_gpu(self):
+        num_samples = 8_388_608
+        batch_size = 512
+        world_size = 4
+
+        steps = self._steps_per_epoch(num_samples, batch_size, world_size)
+        data_seen = steps * batch_size * world_size
+
+        self.assertEqual(data_seen, num_samples)
+
+    def test_default_production_values(self):
+        steps = self._steps_per_epoch(67_108_864, 512, 1)
+        self.assertEqual(steps, 131072)
+        self.assertEqual(steps * 512, 67_108_864)
diff --git a/code/training/train.py b/code/training/train.py
index 1929baa..89e1151 100644
--- a/code/training/train.py
+++ b/code/training/train.py
@@ -1295,7 +1295,7 @@ def _print_gen(name, g):
             per_device_batch_size = get_current_per_device_batch_size(epoch, cfg)
             accumulate_steps = cfg.train.accumulate_steps
             effective_batch_size = per_device_batch_size * accumulate_steps * dist.world_size
-            steps_per_epoch = effective_num_samples // effective_batch_size
+            steps_per_epoch = effective_num_samples // (per_device_batch_size * dist.world_size)
 
             if dist.rank == 0:
                 print(