diff --git a/vllm_spyre/__init__.py b/vllm_spyre/__init__.py
index 480012d29..ab99eb905 100644
--- a/vllm_spyre/__init__.py
+++ b/vllm_spyre/__init__.py
@@ -16,7 +16,7 @@ def register():
 
 def _init_logging():
     """Setup logging, extending from the vLLM logging config"""
-    config = dict[str, Any]()
+    config: dict[str, Any] = {}
 
     if VLLM_CONFIGURE_LOGGING:
         config = {**DEFAULT_LOGGING_CONFIG}
diff --git a/vllm_spyre_next/examples/torch_spyre_inference.py b/vllm_spyre_next/examples/torch_spyre_inference.py
index 47524aa28..cf129bad8 100644
--- a/vllm_spyre_next/examples/torch_spyre_inference.py
+++ b/vllm_spyre_next/examples/torch_spyre_inference.py
@@ -99,7 +99,7 @@ def main():
     t0 = time.time()
     outputs = llm.generate(prompts, sampling_params)
     print(
-        "Time elaspsed for %d tokens is %.2f sec"
+        "Time elapsed for %d tokens is %.2f sec"
         % (len(outputs[0].outputs[0].token_ids), time.time() - t0)
     )
     print("===============")
diff --git a/vllm_spyre_next/vllm_spyre_next/__init__.py b/vllm_spyre_next/vllm_spyre_next/__init__.py
index 5fda4cc0a..e221fe858 100644
--- a/vllm_spyre_next/vllm_spyre_next/__init__.py
+++ b/vllm_spyre_next/vllm_spyre_next/__init__.py
@@ -23,13 +23,13 @@ def register_ops():
 
 def _init_logging():
     """Setup logging, extending from the vLLM logging config"""
-    config = dict[str, Any]()
+    config: dict[str, Any] = {}
 
     if VLLM_CONFIGURE_LOGGING:
         config = {**DEFAULT_LOGGING_CONFIG}
 
     if VLLM_LOGGING_CONFIG_PATH:
-        # Error checks must be done already in vllm.logger.py
+        # Error checks must already be done in vllm.logger
         with open(VLLM_LOGGING_CONFIG_PATH, encoding="utf-8") as file:
             config = json.loads(file.read())
 
diff --git a/vllm_spyre_next/vllm_spyre_next/custom_ops/rms_norm.py b/vllm_spyre_next/vllm_spyre_next/custom_ops/rms_norm.py
index 4ba164b2d..82bcb434e 100644
--- a/vllm_spyre_next/vllm_spyre_next/custom_ops/rms_norm.py
+++ b/vllm_spyre_next/vllm_spyre_next/custom_ops/rms_norm.py
@@ -16,9 +16,10 @@
 
 Spyre Device Constraints:
     - Minimum batch size: 64 (due to spyre constraint, automatically padded)
-    - Device dtype: float16 (converted for CPU)
-    - Output dtype: bfloat16 (converted on CPU)
-    - Algorithm: Transpose-based computation with torch.ops.spyre.full()
+    - Computations performed in torch.float16:
+      Input (dtype defined by model / user) converted to torch.float16 for
+      operations on spyre and then converted back to original dtype for cpu.
+    - Epsilon as tensor: Instead of a scalar, a tensor is created via torch.full()
 
 Limitations:
     Currently the implementation in `forward_spyre` is similar to the
@@ -113,18 +114,15 @@ def forward_spyre(
         hidden_size: int,
         weight: torch.Tensor | None = None,
         residual: torch.Tensor | None = None,
-        variance_size_override: int | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        """Spyre-optimized RMS norm using transpose-based computation (active implementation).
+        """Spyre-optimized RMS norm implementation.
 
         Based on upstream vLLM's forward_static (vllm/model_executor/layers/layernorm.py)
-        but adapted for Spyre device with transpose operations and torch.ops.spyre.full().
-        Compiled separately via torch.compile in __init__.
+        but adapted for Spyre device. Compiled separately via torch.compile in __init__.
 
         Key differences from upstream:
-            - Uses transpose(-1, -2) for computation efficiency on Spyre
-            - Creates epsilon tensor via torch.ops.spyre.full() instead of scalar
-            - No dtype promotion support (torch-spyre limitation)
+            - Creates epsilon tensor via torch.full() instead of scalar
+            - No dtype promotion support to torch.float32 (torch-spyre limitation)
         """
         if residual is not None:
             x = x + residual
@@ -159,7 +157,7 @@ def _forward_spyre_impl(
             1. Minimum batch size: Pads to 64 if needed
             2. Device transfer: CPU -> Spyre convert to float16
             3. Kernel execution: Calls compiled maybe_compiled_forward_spyre
-            4. Result transfer: Spyre -> CPU, trim padding, convert to bfloat16
+            4. Result transfer: Spyre -> CPU, trim padding, convert to input dtype
 
         Limitations:
             - variance_size_override not implemented (raises NotImplementedError)
@@ -169,7 +167,7 @@ def _forward_spyre_impl(
             residual: Optional residual
 
         Returns:
-            Normalized output [batch_size, hidden_size] in bfloat16
+            Normalized output [batch_size, hidden_size] in input dtype
         """
         x_dtype = x.dtype
         x_device = x.device
diff --git a/vllm_spyre_next/vllm_spyre_next/custom_ops/silu_and_mul.py b/vllm_spyre_next/vllm_spyre_next/custom_ops/silu_and_mul.py
index 2774e8dfa..ffaa8a63b 100644
--- a/vllm_spyre_next/vllm_spyre_next/custom_ops/silu_and_mul.py
+++ b/vllm_spyre_next/vllm_spyre_next/custom_ops/silu_and_mul.py
@@ -15,8 +15,9 @@
     - Separate Compilation: forward_spyre is compiled independently via maybe_compile
 
 Spyre Device Constraints:
-    - Device dtype: float16 (via convert_for_spyre)
-    - Output dtype: matches input dtype (converted on CPU)
+    - Computations performed in torch.float16:
+      Input (dtype defined by model / user) converted to torch.float16 for
+      operations on spyre and then converted back to original dtype for cpu.
 
 Output Shape Note:
     Unlike RMSNorm (same input/output shape), SiluAndMul halves the last dimension:
@@ -66,6 +67,13 @@ def __init__(self, *args, **kwargs):
 
         self._layer_name = register_layer(self, "spyre_siluandmul")
 
+        logger.debug_once(
+            "SpyreSiluAndMul: Dispatch: enabled=%s, Forward method=%s, Compiled=%s",
+            self.enabled(),
+            self._forward_method.__name__,
+            self.maybe_compiled_forward_spyre is not self.forward_spyre,
+        )
+
     def forward_oot(self, x: torch.Tensor) -> torch.Tensor:
         """OOT forward pass using custom op to bypass torch.compile.
 
diff --git a/vllm_spyre_next/vllm_spyre_next/custom_ops/vocab_parallel_embedding.py b/vllm_spyre_next/vllm_spyre_next/custom_ops/vocab_parallel_embedding.py
index 183159331..167ab04f3 100644
--- a/vllm_spyre_next/vllm_spyre_next/custom_ops/vocab_parallel_embedding.py
+++ b/vllm_spyre_next/vllm_spyre_next/custom_ops/vocab_parallel_embedding.py
@@ -95,6 +95,13 @@ def __init__(self, *args, **kwargs):
 
         self._layer_name = register_layer(self, "spyre_vocab_parallel_embedding")
 
+        logger.debug_once(
+            "SpyreVocabParallelEmbedding: Dispatch: enabled=%s, Forward method=%s, Compiled=%s",
+            self.enabled(),
+            self._forward_method.__name__,
+            self.maybe_compiled_forward_spyre is not self.forward_spyre,
+        )
+
     def forward_oot(self, x: torch.Tensor) -> torch.Tensor:
         """OOT forward pass using custom op to bypass torch.compile.
 
diff --git a/vllm_spyre_next/vllm_spyre_next/platform.py b/vllm_spyre_next/vllm_spyre_next/platform.py
index 024d225e7..7c000e110 100644
--- a/vllm_spyre_next/vllm_spyre_next/platform.py
+++ b/vllm_spyre_next/vllm_spyre_next/platform.py
@@ -81,10 +81,8 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
         parallel_config = vllm_config.parallel_config
         if parallel_config.worker_cls == "auto":
             # "auto" defaults to the CPUWorker as we inherit from the CpuPlatform
-            # from vllm_spyre_next.v1.worker.spyre_worker import TorchSpyreWorker
+            # Override with TorchSpyreWorker for Spyre-specific functionality
             worker_class = "vllm_spyre_next.v1.worker.spyre_worker.TorchSpyreWorker"
-            # if a torch spyre specific worker class is needed it can be loaded with
-            # worker_class = "vllm_spyre_next.v1.worker.spyre_worker.TorchSpyreWorker"
             logger.info("Loading worker from: %s", worker_class)
             parallel_config.worker_cls = worker_class
 
diff --git a/vllm_spyre_next/vllm_spyre_next/testing/pytest_plugin.py b/vllm_spyre_next/vllm_spyre_next/testing/pytest_plugin.py
index 52c44bbf3..eea6b7fe4 100644
--- a/vllm_spyre_next/vllm_spyre_next/testing/pytest_plugin.py
+++ b/vllm_spyre_next/vllm_spyre_next/testing/pytest_plugin.py
@@ -160,7 +160,7 @@ def _get_paths_from_yaml() -> str:
 
 def _cache_root() -> Path:
     """
-    Cache directory for cloned tests (sticky between runs)
+    Cache directory for cloned tests (persists across runs)
     """
     # Respect XDG if present, fallback to ~/.cache
     xdg = os.environ.get("XDG_CACHE_HOME")
@@ -171,7 +171,8 @@ def _cache_root() -> Path:
 def _extract_vllm_commit_from_pyproject() -> str:
     """
     Extract the vLLM git reference from pyproject.toml [tool.uv.sources] section.
-    Returns None if not found or parseable.
+    Raises FileNotFoundError if pyproject.toml is missing, or KeyError
+    if the expected source entry is not found.
     """
     pyproject_path = Path(__file__).parent.parent.parent / "pyproject.toml"
     if not pyproject_path.exists():
@@ -498,7 +499,7 @@ def pytest_collection_modifyitems(config: pytest.Config, items: list[pytest.Item
         elif allow_entry.mode == "xfail_strict":
             item.add_marker(pytest.mark.xfail(strict=True))
 
-    # Reorder tests so that tests with "model" in the name run first
+    # Reorder tests so that tests with "uses_subprocess" marker run first
     _reorder_tests_by_name(items)
 
 
@@ -582,7 +583,7 @@ def _spyre_default_vllm_config(monkeypatch):
         compilation_config=CompilationConfig(custom_ops=["all"]),
     )
     with set_current_vllm_config(config), set_forward_context(None, config):
-        # Set forward context so custom ops can access no_compile_layers
+        # Set forward context so custom ops can access the vllm config
         yield