diff --git a/test/test_ops.py b/test/test_ops.py
index ba5af6e52d..17817dff75 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -537,7 +537,8 @@ class TestOps(unittest.TestCase):
     helper_test_op(None, lambda x,y: x//y, forward_only=True, vals=[[5, 6, 7],[1, 2, 3]])
     helper_test_op(None, lambda x: x/2, forward_only=True, vals=[[3, 4, 5]])
     helper_test_op(None, lambda x: x//2, forward_only=True, vals=[[3, 4, 5]])
-    helper_test_op(None, functools.partial(torch.div, rounding_mode="trunc"), Tensor.idiv, forward_only=True, vals=[[5, -6, 7],[1, 2, 3]])
+    helper_test_op(None, functools.partial(torch.div, rounding_mode="trunc"), Tensor.idiv, forward_only=True,
+                   vals=[[-4, 7, 5, 4, -7, 8], [2, -3, 8, -2, 3, 5]])
     if is_dtype_supported(dtypes.uint64):
       x = Tensor(2**64 - 1, dtype=dtypes.uint64).idiv(1)
       np.testing.assert_equal(x.numpy(), 2**64 - 1)
diff --git a/tinygrad/ops.py b/tinygrad/ops.py
index f7996c0272..e4138845c6 100644
--- a/tinygrad/ops.py
+++ b/tinygrad/ops.py
@@ -44,7 +44,7 @@ class SimpleMathTrait:
   def __sub__(self, x): return self.sub(x)
   def __mul__(self, x): return self.mul(x)
   def __truediv__(self, x): return self.div(x)
-  def __floordiv__(self, x): return self.idiv(x)
+  def __floordiv__(self, x): return self.idiv(x)  # TODO: idiv is trunc div, not floordiv
   def __and__(self, x): return self.bitwise_and(x)
   def __or__(self, x): return self.bitwise_or(x)
   def __xor__(self, x): return self.xor(x)
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index cb02e7e173..5f0aa5fbe1 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -3100,10 +3100,10 @@ class Tensor(SimpleMathTrait):
     Divides `self` by `x`.
     Equivalent to `self // x`.
     Supports broadcasting to a common shape, type promotion, and integer inputs.
-    `idiv` performs integer division.
+    `idiv` performs integer division (truncate towards zero).
 
     ```python exec="true" source="above" session="tensor" result="python"
-    print(Tensor([1, 4, 10]).idiv(Tensor([2, 3, 4])).numpy())
+    print(Tensor([-4, 7, 5, 4, -7, 8]).idiv(Tensor([2, -3, 8, -2, 3, 5])).numpy())
     ```
     """
     return F.IDiv.apply(*self._broadcasted(x, reverse))