diff --git a/jax_scaled_arithmetics/core/interpreters.py b/jax_scaled_arithmetics/core/interpreters.py
index b0948c1..6a83a1b 100644
--- a/jax_scaled_arithmetics/core/interpreters.py
+++ b/jax_scaled_arithmetics/core/interpreters.py
@@ -6,7 +6,12 @@
 import jax
 import numpy as np
 from jax import core
-from jax._src.custom_derivatives import custom_jvp_call_jaxpr_p, custom_jvp_call_p, custom_vjp_call_p
+from jax._src.custom_derivatives import (
+    custom_jvp_call_jaxpr_p,
+    custom_jvp_call_p,
+    custom_vjp_call_jaxpr_p,
+    custom_vjp_call_p,
+)
 from jax._src.util import safe_map
 
 from .datatype import NDArray, ScaledArray, as_scaled_array_base, is_scaled_leaf
@@ -287,7 +292,12 @@ def scaled_custom_vjp_call_translation(*args: ScaledArray, **params: Any) -> Seq
     """Scaled translation of `custom_vjp_call` primitive. Forwarding the scaled call to sub-jaxpr,
     and modifying the underlying `vjp` function.
     """
-    raise NotImplementedError("Scaled custom VJP primitive not yet supported.")
+    key_jaxpr = "fun_jaxpr"
+    call_closed_jaxpr = params[key_jaxpr]
+    # FIXME: re-call the custom_vjp decorator/bind.
+    call_subfunc = partial(autoscale_jaxpr, call_closed_jaxpr.jaxpr, call_closed_jaxpr.literals)
+    return call_subfunc(*args)
 
 
 register_scaled_op(custom_vjp_call_p, scaled_custom_vjp_call_translation)
+register_scaled_op(custom_vjp_call_jaxpr_p, scaled_custom_vjp_call_translation)
diff --git a/tests/core/test_interpreter.py b/tests/core/test_interpreter.py
index b24092b..06d87a1 100644
--- a/tests/core/test_interpreter.py
+++ b/tests/core/test_interpreter.py
@@ -171,6 +171,42 @@ def fn(x, y):
         npt.assert_array_almost_equal(scaled_primals, primals)
         npt.assert_array_almost_equal(scaled_tangents, tangents)
 
+    @chex.variants(with_jit=False, without_jit=True)
+    def test__autoscale_decorator__custom_vjp__proper_graph_transformation_and_result(self):
+        # JAX official `vjp` example.
+        @jax.custom_vjp
+        def f(x, y):
+            return jnp.sin(x) * y
+
+        def f_fwd(x, y):
+            return f(x, y), (jnp.cos(x), jnp.sin(x), y)
+
+        def f_bwd(res, g):
+            cos_x, sin_x, y = res
+            return (cos_x * g * y, sin_x * g)
+
+        f.defvjp(f_fwd, f_bwd)
+
+        def fn(x, y):
+            primals, f_vjp = jax.vjp(f, x, y)
+            return primals, f_vjp(x * y)
+
+        # `autoscale` on `custom_jvp` method.
+        scaled_inputs = (
+            scaled_array([-2.0, 0.5], 0.5, dtype=np.float32),
+            scaled_array([1.5, -4.5], 2, dtype=np.float32),
+        )
+        scaled_primals, scaled_grads = self.variant(autoscale(fn))(*scaled_inputs)
+        # JAX default/expected values
+        inputs = tuple(map(asarray, scaled_inputs))
+        primals, grads = self.variant(fn)(*inputs)
+
+        assert isinstance(scaled_primals, ScaledArray)
+        npt.assert_array_almost_equal(scaled_primals, primals)
+        for g, sg in zip(grads, scaled_grads):
+            assert isinstance(sg, ScaledArray)
+            npt.assert_array_almost_equal(sg, g)
+
     @parameterized.parameters(
         {"input": 3.0},
         {"input": np.float32(3.0)},