diff --git a/core/src/main/scala/torch/DType.scala b/core/src/main/scala/torch/DType.scala
index f32e3773..18b33c6d 100644
--- a/core/src/main/scala/torch/DType.scala
+++ b/core/src/main/scala/torch/DType.scala
@@ -390,8 +390,10 @@ type NumericPromoted[D <: DType] <: DType = D match
 
 /** Promoted type for tensor operations that always output floats (e.g. `sin`) */
 type FloatPromoted[D <: DType] <: FloatNN = D match
-  case Float64 => Float64
-  case _       => Float32
+  case Float16  => Float16
+  case BFloat16 => BFloat16
+  case Float64  => Float64
+  case _        => Float32
 
 /** Demoted type for complex to real type extractions (e.g. `imag`, `real`) */
 type ComplexToReal[D <: DType] <: DType = D match
diff --git a/core/src/main/scala/torch/ops/ReductionOps.scala b/core/src/main/scala/torch/ops/ReductionOps.scala
index fba5af41..476ce085 100644
--- a/core/src/main/scala/torch/ops/ReductionOps.scala
+++ b/core/src/main/scala/torch/ops/ReductionOps.scala
@@ -270,12 +270,13 @@ def min[D <: RealNN](input: Tensor[D], dim: Long, keepdim: Boolean = false): Ten
   * @param p
   *   the norm to be computed
   */
-// TODO dtype promotion floatNN/complexNN => highest floatNN
 def dist[D <: NumericNN, D2 <: NumericNN](
     input: Tensor[D],
     other: Tensor[D2],
     p: Float = 2
-)(using AtLeastOneFloat[D, D2]): Tensor[D] =
+)(using
+    AtLeastOneFloat[D, D2]
+): Tensor[Promoted[FloatPromoted[ComplexToReal[D]], FloatPromoted[ComplexToReal[D2]]]] =
   Tensor(torchNative.dist(input.native, other.native, toScalar(p)))
 
 /** Returns the log of summed exponentials of each row of the `input` tensor in the given dimension