diff --git a/rlax/_src/value_learning_test.py b/rlax/_src/value_learning_test.py
index 21f2487..22fd451 100644
--- a/rlax/_src/value_learning_test.py
+++ b/rlax/_src/value_learning_test.py
@@ -896,6 +896,108 @@ def test_quantile_expected_sarsa_batch_uniform(self, huber_param):
         self.uniform_expected[huber_param], actual, rtol=1e-5)
 
 
+class StopTargetGradientsDefaultTest(absltest.TestCase):
+  """Regression tests for stop_target_gradients=True default in value_learning.
+
+  All value_learning functions default to stop_target_gradients=True, which
+  means gradients do NOT flow through the bootstrap targets.  This is the
+  correct behaviour for standard TD-learning and matches vtrace.py.
+
+  These tests verify:
+  1. Default (True): gradient w.r.t. bootstrap value v_t is zero.
+  2. Explicit False: gradient does flow through v_t (meta-gradient path).
+  3. Forward values are identical regardless of the flag.
+  """
+
+  # value_learning functions operate on scalars; use vmap for batched tests.
+
+  # ── td_learning ────────────────────────────────────────────────────────────
+
+  def test_td_learning_default_stops_gradient_on_v_t(self):
+    """Default stop_target_gradients=True: grad wrt v_t is zero."""
+    v_tm1 = jnp.array([1.0, 2.0, 3.0])
+    r_t   = jnp.array([0.5, -0.5, 1.0])
+    disc  = jnp.array([0.9, 0.8, 1.0])
+    v_t   = jnp.array([1.5, 2.5, 2.0])
+    batched = jax.vmap(value_learning.td_learning)
+    def fn(vt):
+      return batched(v_tm1, r_t, disc, vt).sum()
+    grad = jax.grad(fn)(v_t)
+    np.testing.assert_array_equal(grad, jnp.zeros_like(v_t))
+
+  def test_td_learning_explicit_false_passes_gradient(self):
+    """stop_target_gradients=False: gradient does flow through v_t."""
+    v_tm1 = jnp.array([1.0, 2.0])
+    r_t   = jnp.array([0.5, -0.5])
+    disc  = jnp.array([0.9, 0.8])
+    v_t   = jnp.array([1.5, 2.5])
+    batched = jax.vmap(functools.partial(
+        value_learning.td_learning, stop_target_gradients=False))
+    def fn(vt):
+      return batched(v_tm1, r_t, disc, vt).sum()
+    grad = jax.grad(fn)(v_t)
+    self.assertFalse(jnp.all(grad == 0))
+
+  def test_td_learning_forward_unchanged(self):
+    """Forward values must be identical regardless of stop_target_gradients."""
+    v_tm1 = jnp.array([1.0, 2.0])
+    r_t   = jnp.array([0.5, -0.5])
+    disc  = jnp.array([0.9, 0.8])
+    v_t   = jnp.array([1.5, 2.5])
+    batched_true  = jax.vmap(value_learning.td_learning)
+    batched_false = jax.vmap(functools.partial(
+        value_learning.td_learning, stop_target_gradients=False))
+    np.testing.assert_allclose(
+        batched_true(v_tm1, r_t, disc, v_t),
+        batched_false(v_tm1, r_t, disc, v_t), atol=1e-6)
+
+  # ── sarsa ─────────────────────────────────────────────────────────────────
+  # sarsa(q_tm1, a_tm1, r_t, discount_t, q_t, a_t, stop_target_gradients)
+
+  def test_sarsa_default_stops_gradient_on_q_t(self):
+    """Default stop_target_gradients=True: grad wrt q_t is zero."""
+    q_tm1 = jnp.array([[1.0, 2.0], [3.0, 1.5]])
+    a_tm1 = jnp.array([0, 1])
+    r_t   = jnp.array([0.5, -0.5])
+    disc  = jnp.array([0.9, 0.8])
+    q_t   = jnp.array([[1.5, 2.5], [2.0, 1.0]])
+    a_t   = jnp.array([1, 0])
+    batched = jax.vmap(value_learning.sarsa)
+    def fn(qt):
+      return batched(q_tm1, a_tm1, r_t, disc, qt, a_t).sum()
+    grad = jax.grad(fn)(q_t)
+    np.testing.assert_array_equal(grad, jnp.zeros_like(q_t))
+
+  def test_sarsa_forward_unchanged(self):
+    q_tm1 = jnp.array([[1.0, 2.0], [3.0, 1.5]])
+    a_tm1 = jnp.array([0, 1])
+    r_t   = jnp.array([0.5, -0.5])
+    disc  = jnp.array([0.9, 0.8])
+    q_t   = jnp.array([[1.5, 2.5], [2.0, 1.0]])
+    a_t   = jnp.array([1, 0])
+    batched_true  = jax.vmap(value_learning.sarsa)
+    batched_false = jax.vmap(functools.partial(
+        value_learning.sarsa, stop_target_gradients=False))
+    np.testing.assert_allclose(
+        batched_true(q_tm1, a_tm1, r_t, disc, q_t, a_t),
+        batched_false(q_tm1, a_tm1, r_t, disc, q_t, a_t), atol=1e-6)
+
+  # ── q_learning ────────────────────────────────────────────────────────────
+
+  def test_q_learning_default_stops_gradient_on_q_t(self):
+    """Default stop_target_gradients=True: grad wrt q_t is zero."""
+    q_tm1 = jnp.array([[1.0, 2.0], [3.0, 1.5]])
+    a_tm1 = jnp.array([0, 1])
+    r_t   = jnp.array([0.5, -0.5])
+    disc  = jnp.array([0.9, 0.8])
+    q_t   = jnp.array([[1.5, 2.5], [2.0, 1.0]])
+    batched = jax.vmap(value_learning.q_learning)
+    def fn(qt):
+      return batched(q_tm1, a_tm1, r_t, disc, qt).sum()
+    grad = jax.grad(fn)(q_t)
+    np.testing.assert_array_equal(grad, jnp.zeros_like(q_t))
+
+
 if __name__ == '__main__':
   jax.config.update('jax_numpy_rank_promotion', 'raise')
   absltest.main()