diff --git a/onnxscript/function_libs/torch_lib/ops/core.py b/onnxscript/function_libs/torch_lib/ops/core.py
index b287cec057..594c9d9465 100644
--- a/onnxscript/function_libs/torch_lib/ops/core.py
+++ b/onnxscript/function_libs/torch_lib/ops/core.py
@@ -5085,10 +5085,10 @@ def aten_linear_backward(
 
 @torch_op("aten::linspace", trace_only=True)
 def aten_linspace(
-    start: TFloat,
-    end: TFloat,
+    start: float,
+    end: float,
     steps: int,
-    dtype: int = FLOAT.dtype,
+    dtype: int = -1,
     layout: str = "",
     device: str = "",
     pin_memory: bool = False,
@@ -5098,26 +5098,43 @@ def aten_linspace(
     if dtype == -1 or dtype is None:
         dtype = FLOAT.dtype
 
-    # Reference: https://github.com/pytorch/pytorch/blob/b35ca2cb941b5ba90858322810ca85c31e4541fd/torch/_refs/__init__.py#L4896
     if steps == 0:
         return aten_full(op.Constant(value_ints=[0]), 0.0, dtype=dtype)
     if steps == 1:
         return aten_full(op.Constant(value_ints=[steps]), start, dtype=dtype)
 
-    rg = aten_arange_start(0, steps, dtype=dtype)
-    start = op.Cast(start, to=dtype)
-    end = op.Cast(end, to=dtype)
-    steps_float = op.Cast(steps, to=dtype)
-    one = op.Cast(1.0, to=dtype)
-    two = op.Cast(2.0, to=dtype)
-    steps_minus_1 = op.Cast(steps - 1, to=dtype)
-    step = op.Div(op.Sub(end, start), steps_minus_1)
-    return op.Where(
-        rg < op.Div(steps_float, two),
-        start + step * rg,
-        end - step * (steps_float - one - rg),
+    # For integer output dtypes, cast start/end to the target dtype first
+    # This matches PyTorch's behavior where fractional start/end values
+    # are truncated before computing the linspace
+    if ir.DataType(dtype).is_integer():
+        # Use double precision for computation to match PyTorch's internal precision
+        compute_dtype = ir.DataType.DOUBLE
+        # Cast to integer dtype first, then to compute dtype
+        # This ensures truncation happens before computation
+        start_f = op.Constant(value=ir.tensor(int(start), dtype=compute_dtype))
+        end_f = op.Constant(value=ir.tensor(int(end), dtype=compute_dtype))
+    else:
+        compute_dtype = dtype
+        start_f = op.Constant(value=ir.tensor(start, dtype=compute_dtype))
+        end_f = op.Constant(value=ir.tensor(end, dtype=compute_dtype))
+
+    rg = aten_arange_start(0, steps, dtype=compute_dtype)
+    steps_f = op.Constant(value=ir.tensor(steps, dtype=compute_dtype))
+    one = op.Constant(value=ir.tensor(1, dtype=compute_dtype))
+    two = op.Constant(value=ir.tensor(2, dtype=compute_dtype))
+    steps_minus_1 = op.Constant(value=ir.tensor(steps - 1, dtype=compute_dtype))
+    step = op.Constant(value=ir.tensor((end - start) / (steps - 1), dtype=compute_dtype))
+
+    # Two-sided computation for numerical stability at endpoints
+    # Use forward computation for first half, backward for second half
+    lin_vals = op.Where(
+        rg < op.Div(steps_f, two),
+        op.Add(start_f, op.Mul(step, rg)),
+        op.Sub(end_f, op.Mul(step, op.Sub(steps_minus_1, rg))),
     )
 
+    return op.Cast(lin_vals, to=dtype)
+
 
 @torch_op("aten::log", trace_only=True)
 def aten_log(self: TFloat) -> TFloat:
diff --git a/tests/function_libs/torch_lib/ops_test_data.py b/tests/function_libs/torch_lib/ops_test_data.py
index e87a0cc232..93c2d1045d 100644
--- a/tests/function_libs/torch_lib/ops_test_data.py
+++ b/tests/function_libs/torch_lib/ops_test_data.py
@@ -777,14 +777,6 @@ def _where_input_wrangler(
         "linspace",
         core_ops.aten_linspace,
         tolerance={torch.float16: (2e-2, 2e-3)},
-    )
-    .xfail(
-        dtypes=(torch.int64, torch.int32),
-        reason="fixme: Results do not match with PyTorch. https://github.com/microsoft/onnxscript/issues/854",
-    )
-    .skip(
-        matcher=lambda sample: sample.kwargs.get("dtype") in (torch.int64, torch.int32),
-        reason="fixme: Results do not match with PyTorch. https://github.com/microsoft/onnxscript/issues/854",
     ),
     TorchLibOpInfo("log", core_ops.aten_log),
     TorchLibOpInfo("le", core_ops.aten_le),