diff --git a/intermediate_source/torch_compile_tutorial.rst b/intermediate_source/torch_compile_tutorial.rst
index b8a824f43b9..c8b1c6567bc 100644
--- a/intermediate_source/torch_compile_tutorial.rst
+++ b/intermediate_source/torch_compile_tutorial.rst
@@ -172,7 +172,7 @@ see a significant improvement compared to eager.
 
 And indeed, we can see that running our model with ``torch.compile``
 results in a significant speedup. On an NVIDIA A100 GPU, we observe a
-2.2x speedup. Speedup mainly comes from reducing Python overhead and
+2.3x speedup. Speedup mainly comes from reducing Python overhead and
 GPU read/writes, and so the observed speedup may vary on factors such as model
 architecture and batch size. For example, if a model's architecture is simple
 and the amount of data is large, then the bottleneck would be
@@ -196,16 +196,16 @@ Now, let's consider comparing training.
     opt = torch.optim.Adam(model.parameters())
 
     def train(mod, data):
+        opt.zero_grad(True)
         pred = mod(data[0])
         loss = torch.nn.CrossEntropyLoss()(pred, data[1])
         loss.backward()
+        opt.step()
 
     eager_times = []
     for i in range(N_ITERS):
         inp = generate_data(16)
-        opt.zero_grad(True)
         _, eager_time = timed(lambda: train(model, inp))
-        opt.step()
         eager_times.append(eager_time)
         print(f"eager train time {i}: {eager_time}")
     print("~" * 10)
@@ -217,9 +217,7 @@ Now, let's consider comparing training.
     compile_times = []
     for i in range(N_ITERS):
         inp = generate_data(16)
-        opt.zero_grad(True)
         _, compile_time = timed(lambda: train_opt(model, inp))
-        opt.step()
         compile_times.append(compile_time)
         print(f"compile train time {i}: {compile_time}")
     print("~" * 10)
@@ -233,13 +231,7 @@ Now, let's consider comparing training.
 Again, we can see that ``torch.compile`` takes longer in the first
 iteration, as it must compile the model, but afterward, we see
 significant speedups compared to eager. On an NVIDIA A100 GPU, we
-observe a 1.8x speedup.
-
-One thing to note is that, as of now, we cannot place optimizer code --
-``opt.zero_grad`` and ``opt.step`` -- inside of an optimized function.
-The rest of the training loop -- the forward pass and the backward pass --
-can be optimized. We are currently working on enabling optimizers to be
-compatible with ``torch.compile``.
+observe a 2.2x speedup.
 
 Comparison to TorchScript and FX Tracing
 -----------------------------------------
diff --git a/intermediate_source/torch_compile_tutorial_.py b/intermediate_source/torch_compile_tutorial_.py
index 69e96c8b3b1..3b7c6884a2a 100644
--- a/intermediate_source/torch_compile_tutorial_.py
+++ b/intermediate_source/torch_compile_tutorial_.py
@@ -175,7 +175,7 @@ def evaluate(mod, inp):
 ######################################################################
 # And indeed, we can see that running our model with ``torch.compile``
 # results in a significant speedup. On an NVIDIA A100 GPU, we observe a
-# 2.2x speedup. Speedup mainly comes from reducing Python overhead and
+# 2.3x speedup. Speedup mainly comes from reducing Python overhead and
 # GPU read/writes, and so the observed speedup may vary on factors such as model
 # architecture and batch size. For example, if a model's architecture is simple
 # and the amount of data is large, then the bottleneck would be
@@ -197,16 +197,16 @@ def evaluate(mod, inp):
 opt = torch.optim.Adam(model.parameters())
 
 def train(mod, data):
+    opt.zero_grad(True)
     pred = mod(data[0])
     loss = torch.nn.CrossEntropyLoss()(pred, data[1])
     loss.backward()
+    opt.step()
 
 eager_times = []
 for i in range(N_ITERS):
     inp = generate_data(16)
-    opt.zero_grad(True)
     _, eager_time = timed(lambda: train(model, inp))
-    opt.step()
     eager_times.append(eager_time)
     print(f"eager train time {i}: {eager_time}")
 print("~" * 10)
@@ -218,9 +218,7 @@ def train(mod, data):
 compile_times = []
 for i in range(N_ITERS):
     inp = generate_data(16)
-    opt.zero_grad(True)
     _, compile_time = timed(lambda: train_opt(model, inp))
-    opt.step()
     compile_times.append(compile_time)
     print(f"compile train time {i}: {compile_time}")
 print("~" * 10)
@@ -235,13 +233,7 @@ def train(mod, data):
 # Again, we can see that ``torch.compile`` takes longer in the first
 # iteration, as it must compile the model, but afterward, we see
 # significant speedups compared to eager. On an NVIDIA A100 GPU, we
-# observe a 1.8x speedup.
-#
-# One thing to note is that, as of now, we cannot place optimizer code --
-# ``opt.zero_grad`` and ``opt.step`` -- inside of an optimized function.
-# The rest of the training loop -- the forward pass and the backward pass --
-# can be optimized. We are currently working on enabling optimizers to be
-# compatible with ``torch.compile``.
+# observe a 2.2x speedup.
 
 ######################################################################
 # Comparison to TorchScript and FX Tracing