diff --git a/onnxscript/function_libs/torch_lib/ops/core.py b/onnxscript/function_libs/torch_lib/ops/core.py
index adf1bad4b6..231faaf83c 100644
--- a/onnxscript/function_libs/torch_lib/ops/core.py
+++ b/onnxscript/function_libs/torch_lib/ops/core.py
@@ -56,6 +56,9 @@
 _INT64_MAX = 9223372036854775807
 _INT64_MIN = -9223372036854775808
 _MATH_PI = math.pi
+_INT64_ARANGE_NON_INTEGRAL_USES_FLOAT_LENGTH = (
+    torch.arange(3.1, dtype=torch.int64).numel() == 4
+)
 
 
 @torch_op("aten::_local_scalar_dense", trace_only=True)
@@ -521,6 +524,56 @@ def _range_supported(dtype: int) -> bool:
     }
 
 
+def _is_integral_dtype(dtype: int) -> bool:
+    return dtype in {
+        INT8.dtype,
+        INT16.dtype,
+        INT32.dtype,
+        INT64.dtype,
+    }
+
+
+def _is_integral_scalar(arg: TRealUnlessFloat16OrInt8) -> bool:
+    if isinstance(arg, int):
+        return True
+    if isinstance(arg, float):
+        return False
+    return arg.dtype in {
+        INT8.dtype,
+        INT16.dtype,
+        INT32.dtype,
+        INT64.dtype,
+    }
+
+
+def _arange_integral_dtype_with_non_integral_args(
+    start: TRealUnlessFloat16OrInt8,
+    end: TRealUnlessFloat16OrInt8,
+    step: TRealUnlessFloat16OrInt8,
+    dtype: int,
+) -> TensorType:
+    """Implements torch.arange for integral dtypes when not all inputs are integral."""
+    start_float = op.Cast(start, to=FLOAT.dtype)
+    end_float = op.Cast(end, to=FLOAT.dtype)
+    step_float = op.Cast(step, to=FLOAT.dtype)
+
+    length = op.Cast(
+        op.Ceil(op.Div(op.Sub(end_float, start_float), step_float)), to=INT64.dtype
+    )
+    index = op.Range(op.Constant(value_int=0), length, op.Constant(value_int=1))
+
+    if _range_supported(dtype):
+        index = op.Cast(index, to=dtype)
+        start = op.Cast(start, to=dtype)
+        step = op.Cast(step, to=dtype)
+        return op.Add(start, op.Mul(step, index))
+
+    start = op.Cast(start, to=INT64.dtype)
+    step = op.Cast(step, to=INT64.dtype)
+    result = op.Add(start, op.Mul(step, index))
+    return op.Cast(result, to=dtype)
+
+
 def _integral_to_be_adjusted(dtype: int) -> bool:
     """Returns true if the dtype is special integral handled by torch."""
     return dtype in {
@@ -544,6 +597,12 @@ def aten_arange(
         zero = op.CastLike(0.0, end)
         one = op.CastLike(1.0, end)
         result = op.Range(zero, end, one)
+    elif (
+        _is_integral_dtype(dtype)
+        and not _is_integral_scalar(end)
+        and (dtype != INT64.dtype or _INT64_ARANGE_NON_INTEGRAL_USES_FLOAT_LENGTH)
+    ):
+        result = _arange_integral_dtype_with_non_integral_args(0, end, 1, dtype)
     elif _range_supported(dtype):
         end = op.Cast(end, to=dtype)
         zero = op.Cast(0, to=dtype)
@@ -576,6 +635,12 @@ def aten_arange_start(
     if dtype == -1 or dtype is None:
         one = op.CastLike(1.0, end)
         result = op.Range(start, end, one)
+    elif (
+        _is_integral_dtype(dtype)
+        and not (_is_integral_scalar(start) and _is_integral_scalar(end))
+        and (dtype != INT64.dtype or _INT64_ARANGE_NON_INTEGRAL_USES_FLOAT_LENGTH)
+    ):
+        result = _arange_integral_dtype_with_non_integral_args(start, end, 1, dtype)
     elif _range_supported(dtype):
         end = op.Cast(end, to=dtype)
         start = op.Cast(start, to=dtype)
@@ -659,17 +724,26 @@ def aten_arange_start_step(
             end = op.Cast(end, to=FLOAT.dtype)
             step = op.Cast(step, to=FLOAT.dtype)
             result = op.Range(start, end, step)
-    elif _integral_to_be_adjusted(dtype):
-        # PyTorch arange op handles these integral types differently from INT64,
-        # so we have to adjust these arguments accordingly.
-        # https://github.com/pytorch/pytorch/blob/121cfb60c0817816fcbe2190303b7f6d05c77cf3/torch/_refs/__init__.py#L4794
-        start, end, step = _adjust_args_for_arange_int_dtype(start, end, step)
-        result = op.Cast(op.Range(start, end, step), to=dtype)
+    elif (
+        _is_integral_dtype(dtype)
+        and not (
+            _is_integral_scalar(start)
+            and _is_integral_scalar(end)
+            and _is_integral_scalar(step)
+        )
+        and (dtype != INT64.dtype or _INT64_ARANGE_NON_INTEGRAL_USES_FLOAT_LENGTH)
+    ):
+        result = _arange_integral_dtype_with_non_integral_args(start, end, step, dtype)
     elif dtype == INT64.dtype:
         end = op.Cast(end, to=dtype)
         start = op.Cast(start, to=dtype)
         step = op.Cast(step, to=dtype)
         result = op.Range(start, end, step)
+    elif _integral_to_be_adjusted(dtype):
+        # PyTorch arange op handles these integral types differently from INT64
+        # when all arguments are integral.
+        start, end, step = _adjust_args_for_arange_int_dtype(start, end, step)
+        result = op.Cast(op.Range(start, end, step), to=dtype)
     else:
         # Cast input to float if dtype is not supported by Range,
         # because the input dtype may be e.g. bfloat16,
@@ -5804,8 +5878,9 @@ def aten_logit(self: TFloat, eps: Optional[float] = None) -> TFloat:
     one_minus_eps = ir.tensor(1 - eps, dtype=self.dtype)
     eps = ir.tensor(eps, dtype=self.dtype)
 
-    temporary_self = op.Where(self <= one_minus_eps, self, one_minus_eps)
-    z = op.Where(temporary_self < eps, eps, temporary_self)
+    # Match torch.clamp behavior for eps > 0.5 by applying max then min.
+    z = op.Where(self < eps, eps, self)
+    z = op.Where(z <= one_minus_eps, z, one_minus_eps)
 
     return op.Log(op.Div(z, op.Sub(one, z)))