diff --git a/.github/workflows/mlx-ci.yml b/.github/workflows/mlx-ci.yml
index 75940832a05..73e84148558 100644
--- a/.github/workflows/mlx-ci.yml
+++ b/.github/workflows/mlx-ci.yml
@@ -351,7 +351,7 @@ jobs:
           echo "OK: Studio prebuilt llama.cpp on Mac M1 + GGUF /completion works"
 
       # Real MLX training + inference smoke test. Trains
-      # unsloth/gemma-3-270m-it for 7 deterministic LoRA steps
+      # unsloth/gemma-3-270m-it for 30 deterministic LoRA steps
       # (batch_size=2, gradient_accumulation_steps=3) on a single
       # repeated row ("<<HELLO!!>> My name is Unsloth!"), then saves
       # the trained model in 3 export formats. The `train` subcommand
diff --git a/tests/studio/run_real_mlx_smoke.py b/tests/studio/run_real_mlx_smoke.py
index 27f682ee4ec..45343bec99f 100644
--- a/tests/studio/run_real_mlx_smoke.py
+++ b/tests/studio/run_real_mlx_smoke.py
@@ -12,15 +12,15 @@
 
 The `train` subcommand:
   1. Loads `unsloth/gemma-3-270m-it` via FastMLXModel.from_pretrained.
-  2. Applies LoRA r=8 on q/k/v/o.
+  2. Applies LoRA r=8 on q/k/v/o + gate/up/down.
   3. Computes pre-training loss + grad norm via mx.nn.value_and_grad.
-  4. Trains 7 deterministic steps on a dataset of the SAME row repeated
+  4. Trains 30 deterministic steps on a dataset of the SAME row repeated
      ("<<HELLO!!>> My name is Unsloth!"), with batch_size=2 and
      gradient_accumulation_steps=3 so each step processes 6 sequences
-     and the run sees 42 sequences total.
+     and the run sees 180 sequences total.
   5. Computes post-training loss + grad norm.
-  6. Generates from "<<HELLO!!>> My name is " and asserts "Unsloth"
-     appears in the in-memory completion.
+  6. Generates from "<<HELLO!!>> My name is " for regression telemetry
+     and hard-gates the teacher-forced completion loss.
   7. Saves the trained model in three formats:
        - LoRA adapter (save_pretrained_merged save_method="lora")
        - Merged 16-bit (save_pretrained_merged save_method="merged_16bit")
@@ -390,7 +390,9 @@ def _on_step(
         )
         if k in train_result
     }
-    assert len(losses_per_step) == 7, f"expected 7 logged steps, got {losses_per_step}"
+    assert (
+        len(losses_per_step) == config.max_steps
+    ), f"expected {config.max_steps} logged steps, got {losses_per_step}"
     for i, l in enumerate(losses_per_step):
         # Allow exact 0.0: fp16 per-step loss underflows to 0.0 after
         # the LoRA reaches loss=0 around step ~10 with this fixture +