diff --git a/acestep/training/trainer.py b/acestep/training/trainer.py
index 1f037bbc..e7318770 100644
--- a/acestep/training/trainer.py
+++ b/acestep/training/trainer.py
@@ -384,13 +384,24 @@ def __init__(
             self.lora_info = {}
             logger.warning("PEFT not available, training without LoRA adapters")
             
-        # Added Torch Compile Logic
-        if hasattr(torch, "compile") and self.device_type == "cuda":
-            logger.info("Compiling DiT decoder...")
-            self.model.decoder = torch.compile(self.model.decoder, mode="default") # 'default' is more stable for LoRA
-            logger.info("torch.compile successful")
+        # torch.compile: optional perf optimization.
+        # PEFT LoRA wraps the decoder in PeftModelForFeatureExtraction which is
+        # incompatible with torch.compile/inductor on PyTorch 2.7.x
+        # (AssertionError at first forward pass, not at compile time).
+        # Only compile when NOT using PEFT adapters.
+        has_peft = bool(self.lora_info)
+        if hasattr(torch, "compile") and self.device_type == "cuda" and not has_peft:
+            try:
+                logger.info("Compiling DiT decoder...")
+                self.model.decoder = torch.compile(self.model.decoder, mode="default")
+                logger.info("torch.compile successful")
+            except Exception as e:
+                logger.warning(f"torch.compile failed ({e}), continuing without compilation")
         else:
-            logger.warning("torch.compile is not available on this PyTorch version.")
+            if has_peft:
+                logger.info("Skipping torch.compile (incompatible with PEFT LoRA adapters)")
+            else:
+                logger.info("torch.compile not available on this device/PyTorch version, skipping")
         
         # Model config for flow matching
         self.config = model.config