diff --git a/baseline/gpt2_small/muon_speedrun/optim.py b/baseline/gpt2_small/muon_speedrun/optim.py
index c97af7c..8610314 100644
--- a/baseline/gpt2_small/muon_speedrun/optim.py
+++ b/baseline/gpt2_small/muon_speedrun/optim.py
@@ -140,8 +140,11 @@ def make_stock_optimizers(model, rt, kind):
             else:
                 role = 'other'
                 multiplier = SGD_OTHER_MULTIPLIER
+            # Keep matrix decay and bias/LayerNorm no-decay groups separate even
+            # when they share a blockwise LR multiplier.
             decay = SGD_WEIGHT_DECAY if p.ndim >= 2 else 0.0
-            grouped[(role, multiplier, decay)].append(p)
+            decay_role = role + ('_matrices' if p.ndim >= 2 else '_bias_and_layernorm')
+            grouped[(decay_role, multiplier, decay)].append(p)
         groups = []
         for (role, multiplier, decay), params in grouped.items():
             peak = SGD_PEAK_LR * multiplier
diff --git a/baseline/gpt2_small/muon_speedrun/test_sgd_momentum.py b/baseline/gpt2_small/muon_speedrun/test_sgd_momentum.py
index 5772699..3547c30 100644
--- a/baseline/gpt2_small/muon_speedrun/test_sgd_momentum.py
+++ b/baseline/gpt2_small/muon_speedrun/test_sgd_momentum.py
@@ -38,9 +38,10 @@ def test_tied_parameter_once_and_classical_momentum(self):
         self.assertEqual(len(params), len(set(map(id, params))))
         self.assertEqual(set(map(id, params)), set(map(id, model.parameters())))
         groups = {g['role']:g for g in opt.param_groups}
-        self.assertAlmostEqual(groups['block_0']['lr_multiplier'], SGD_BLOCK_MULTIPLIERS[0])
-        self.assertEqual(groups['block_0']['weight_decay'], SGD_WEIGHT_DECAY)
-        self.assertEqual(groups['other']['weight_decay'], 0.0)
+        self.assertAlmostEqual(groups['block_0_matrices']['lr_multiplier'], SGD_BLOCK_MULTIPLIERS[0])
+        self.assertEqual(groups['block_0_matrices']['weight_decay'], SGD_WEIGHT_DECAY)
+        self.assertEqual(groups['block_0_bias_and_layernorm']['weight_decay'], 0.0)
+        self.assertEqual(groups['other_bias_and_layernorm']['weight_decay'], 0.0)
         self.assertTrue(all(g["momentum"] == SGD_MOMENTUM for g in opt.param_groups))
         self.assertTrue(all(not g["nesterov"] for g in opt.param_groups))
         self.assertEqual(optimizer_metadata(opt)["class"], "torch.optim.SGD")