diff --git a/blackjax/smc/waste_free.py b/blackjax/smc/waste_free.py
index 2f0ced582..9727567e9 100644
--- a/blackjax/smc/waste_free.py
+++ b/blackjax/smc/waste_free.py
@@ -67,4 +67,6 @@ def reshape_step_particles(x):
 
 
 def waste_free_smc(n_particles, p):
+    if not n_particles % p ==0:
+        raise ValueError("p must be a divider of n_particles ")
     return functools.partial(update_waste_free, num_resampled=int(n_particles / p), p=p)
diff --git a/tests/smc/test_waste_free_smc.py b/tests/smc/test_waste_free_smc.py
index 8b470aceb..886d08492 100644
--- a/tests/smc/test_waste_free_smc.py
+++ b/tests/smc/test_waste_free_smc.py
@@ -1,4 +1,5 @@
 """Test the tempered SMC steps and routine"""
+
 import functools
 
 import chex
@@ -7,11 +8,14 @@
 import numpy as np
 import pytest
 from absl.testing import absltest
+from scipy.stats import stats
 
 import blackjax
 import blackjax.smc.resampling as resampling
 from blackjax import adaptive_tempered_smc, tempered_smc
-from blackjax.smc import extend_params
+from blackjax.mcmc.random_walk import build_rmh
+from blackjax.smc import extend_params, base
+from blackjax.smc.base import SMCState
 from blackjax.smc.waste_free import update_waste_free, waste_free_smc
 from tests.smc import SMCLinearRegressionTestCase
 from tests.smc.test_tempered_smc import inference_loop
@@ -105,6 +109,44 @@ def test_adaptive_tempered_smc(self):
         self.assert_linear_regression_test_case(result)
 
 
+class Update_waste_free_multivariate_particles(chex.TestCase):
+
+    @chex.variants(with_jit=True)
+    def test_update_waste_free_multivariate_particles(self):
+        """
+        Given resampled multivariate particles,
+        when updating with waste free, they are joined
+        by the result of iterating the MCMC chain to
+        get a bigger set of particles.
+        """
+        resampled_particles = np.ones((50, 3))
+        n_particles = 100
+
+        def normal_logdensity(x):
+            return jnp.log(
+                jax.scipy.stats.multivariate_normal.pdf(
+                    x, mean=np.zeros(3), cov=np.diag(np.ones(3))
+                )
+            )
+
+        def rmh_proposal_distribution(rng_key, position):
+            return position + jax.random.normal(rng_key, (3,)) * 25.0
+
+        kernel = functools.partial(
+            blackjax.rmh.build_kernel(), transition_generator=rmh_proposal_distribution
+        )
+        init = blackjax.rmh.init
+        update, _ = waste_free_smc(n_particles, 2)(
+            init, normal_logdensity, kernel, n_particles
+        )
+
+        updated_particles, infos = self.variant(update)(
+            jax.random.split(jax.random.PRNGKey(10), 50), resampled_particles, {}
+        )
+
+        assert updated_particles.shape == (n_particles, 3)
+
+
 def test_waste_free_set_num_mcmc_steps():
     with pytest.raises(ValueError) as exc_info:
         update_waste_free(
@@ -115,5 +157,11 @@ def test_waste_free_set_num_mcmc_steps():
         )
 
 
+def test_waste_free_p_non_divier():
+    with pytest.raises(ValueError) as exc_info:
+        waste_free_smc(100, 3)
+        assert str(exc_info.value).startswith("p must be a divider")
+
+
 if __name__ == "__main__":
     absltest.main()