diff --git a/unsloth_zoo/fused_losses/cross_entropy_loss.py b/unsloth_zoo/fused_losses/cross_entropy_loss.py
index 4e9a8ede3..17276c417 100644
--- a/unsloth_zoo/fused_losses/cross_entropy_loss.py
+++ b/unsloth_zoo/fused_losses/cross_entropy_loss.py
@@ -33,6 +33,23 @@
 TARGET_GB = os.environ.get("UNSLOTH_CE_LOSS_TARGET_GB", None)
 N_CHUNKS = os.environ.get("UNSLOTH_CE_LOSS_N_CHUNKS", None)
 
+# Register grad_and_value_impl in trace_rules as defense-in-depth.
+# grad_impl is registered but grad_and_value_impl is not, which can cause
+# GB0149 "Unsupported functorch tracing attempt" in some configurations.
+try:
+    from torch._dynamo.trace_rules import manual_torch_name_rule_map as _trace_map
+    from torch._dynamo.variables.higher_order_ops import FunctorchHigherOrderVariable as _FHOV
+    _key = "torch._functorch.eager_transforms.grad_and_value_impl"
+    if _key not in _trace_map:
+        _trace_map[_key] = _FHOV
+        torch._dynamo.trace_rules.get_torch_obj_rule_map.cache_clear()
+except Exception:
+    pass
+
+# Module-level flag: None = untested, True = works, False = skip compile.
+_FUSED_CE_COMPILE_SUPPORTED = None if \
+    os.environ.get("UNSLOTH_FUSED_CE_COMPILE_DISABLE", "0") != "1" else False
+
 @functools.cache
 def _get_mapping(autograd):
     parameters = inspect.signature(getattr(autograd, "forward")).parameters
@@ -301,30 +318,100 @@ def accumulate_chunk(
             accumulated_loss.add_(unscaled_loss)
             grad_inputs_j[:] = chunk_grad_input
         pass
-        if torch_compile:
-            accumulate_chunk = torch.compile(
-                accumulate_chunk,
-                dynamic = True,
-                fullgraph = True,
-                options = torch_compile_options,
-            )
+        global _FUSED_CE_COMPILE_SUPPORTED
+        uncompiled_accumulate_chunk = accumulate_chunk
+
+        if torch_compile and _FUSED_CE_COMPILE_SUPPORTED is not False:
+            try:
+                accumulate_chunk = torch.compile(
+                    accumulate_chunk,
+                    dynamic = True,
+                    fullgraph = True,
+                    options = torch_compile_options,
+                )
+            except Exception:
+                _FUSED_CE_COMPILE_SUPPORTED = False
+                accumulate_chunk = uncompiled_accumulate_chunk
+
+        # Probe path: first-ever forward pass, test if compiled version works
+        if _FUSED_CE_COMPILE_SUPPORTED is None and torch_compile and \
+            accumulate_chunk is not uncompiled_accumulate_chunk:
 
-        for (grad_inputs_j, hidden_states_j, labels_j,) in \
-            zip(__grad_inputs, __shift_states, __shift_labels,):
-            accumulate_chunk(
-                n_chunks = n_chunks,
-                grad_inputs_j = grad_inputs_j,
-                grad_lm_head = grad_lm_head,
-                grad_lm_head_bias = grad_lm_head_bias,
-                hidden_states_j = hidden_states_j,
-                lm_head_weight = lm_head_weight,
-                lm_head_bias = lm_head_bias,
-                labels_j = labels_j,
-                divisor = divisor,
-                scaling = scaling,
-                shift_labels = shift_labels,
-                **extra_kwargs,
-            )
+            _iter = iter(zip(__grad_inputs, __shift_states, __shift_labels))
+            grad_inputs_j, hidden_states_j, labels_j = next(_iter)
+            try:
+                accumulate_chunk(
+                    n_chunks = n_chunks,
+                    grad_inputs_j = grad_inputs_j,
+                    grad_lm_head = grad_lm_head,
+                    grad_lm_head_bias = grad_lm_head_bias,
+                    hidden_states_j = hidden_states_j,
+                    lm_head_weight = lm_head_weight,
+                    lm_head_bias = lm_head_bias,
+                    labels_j = labels_j,
+                    divisor = divisor,
+                    scaling = scaling,
+                    shift_labels = shift_labels,
+                    **extra_kwargs,
+                )
+                _FUSED_CE_COMPILE_SUPPORTED = True
+            except Exception:
+                _FUSED_CE_COMPILE_SUPPORTED = False
+                torch._dynamo.reset()
+                accumulated_loss.zero_()
+                if not overwrite:
+                    grad_inputs.zero_()
+                if grad_lm_head is not None: grad_lm_head.zero_()
+                if grad_lm_head_bias is not None: grad_lm_head_bias.zero_()
+                accumulate_chunk = uncompiled_accumulate_chunk
+                accumulate_chunk(
+                    n_chunks = n_chunks,
+                    grad_inputs_j = grad_inputs_j,
+                    grad_lm_head = grad_lm_head,
+                    grad_lm_head_bias = grad_lm_head_bias,
+                    hidden_states_j = hidden_states_j,
+                    lm_head_weight = lm_head_weight,
+                    lm_head_bias = lm_head_bias,
+                    labels_j = labels_j,
+                    divisor = divisor,
+                    scaling = scaling,
+                    shift_labels = shift_labels,
+                    **extra_kwargs,
+                )
+            # Process remaining chunks via fast path
+            for (grad_inputs_j, hidden_states_j, labels_j,) in _iter:
+                accumulate_chunk(
+                    n_chunks = n_chunks,
+                    grad_inputs_j = grad_inputs_j,
+                    grad_lm_head = grad_lm_head,
+                    grad_lm_head_bias = grad_lm_head_bias,
+                    hidden_states_j = hidden_states_j,
+                    lm_head_weight = lm_head_weight,
+                    lm_head_bias = lm_head_bias,
+                    labels_j = labels_j,
+                    divisor = divisor,
+                    scaling = scaling,
+                    shift_labels = shift_labels,
+                    **extra_kwargs,
+                )
+        else:
+            # Fast path: compile status already known, original main branch loop
+            for (grad_inputs_j, hidden_states_j, labels_j,) in \
+                zip(__grad_inputs, __shift_states, __shift_labels,):
+                accumulate_chunk(
+                    n_chunks = n_chunks,
+                    grad_inputs_j = grad_inputs_j,
+                    grad_lm_head = grad_lm_head,
+                    grad_lm_head_bias = grad_lm_head_bias,
+                    hidden_states_j = hidden_states_j,
+                    lm_head_weight = lm_head_weight,
+                    lm_head_bias = lm_head_bias,
+                    labels_j = labels_j,
+                    divisor = divisor,
+                    scaling = scaling,
+                    shift_labels = shift_labels,
+                    **extra_kwargs,
+                )
         pass
         ctx.save_for_backward(grad_inputs, grad_lm_head, grad_lm_head_bias)
         ctx.scaling = scaling