diff --git a/elfi/methods/inference/samplers.py b/elfi/methods/inference/samplers.py
index 09de508a..abfc9cee 100644
--- a/elfi/methods/inference/samplers.py
+++ b/elfi/methods/inference/samplers.py
@@ -459,9 +459,21 @@ def prepare_new_batch(self, batch_index):
         return batch
 
     def _init_new_round(self):
-        round = self.state['round']
 
-        self._update_round_info(round)
+        self._set_rejection_round(self.state['round'])
+
+        if self.state['round'] == 0 and self._quantiles is not None:
+            self._rejection.set_objective(
+                self.objective['n_samples'], quantile=self._quantiles[0])
+        else:
+            if self._quantiles is not None:
+                self._set_threshold()
+            self._rejection.set_objective(
+                self.objective['n_samples'], threshold=self.current_population_threshold)
+
+    def _set_rejection_round(self, round):
+
+        self._update_round_info(self.state['round'])
 
         # Get a subseed for this round for ensuring consistent results for the round
         seed = self.seed if round == 0 else get_sub_seed(self.seed, round)
@@ -474,15 +486,6 @@ def _init_new_round(self):
             seed=seed,
             max_parallel_batches=self.max_parallel_batches)
 
-        if self.state['round'] == 0 and self._quantiles is not None:
-            self._rejection.set_objective(
-                self.objective['n_samples'], quantile=self._quantiles[0])
-        else:
-            if self._quantiles is not None:
-                self._set_threshold()
-            self._rejection.set_objective(
-                self.objective['n_samples'], threshold=self.current_population_threshold)
-
     def _update_round_info(self, round):
         if self.bar:
             reinit_msg = 'ABC-SMC Round {0} / {1}'.format(
@@ -709,15 +712,12 @@ def __init__(self,
         self.q_threshold = q_threshold
         self.initial_quantile = initial_quantile
 
-        if densratio_estimation is None:
-            self.densratio = DensityRatioEstimation(n=100,
-                                                    epsilon=0.001,
-                                                    max_iter=200,
-                                                    abs_tol=0.01,
-                                                    fold=5,
-                                                    optimize=False)
-        else:
-            self.densratio = densratio_estimation
+        self.densratio = densratio_estimation or DensityRatioEstimation(n=100,
+                                                                        epsilon=0.001,
+                                                                        max_iter=200,
+                                                                        abs_tol=0.01,
+                                                                        fold=5,
+                                                                        optimize=False)
 
     def set_objective(self,
                       n_samples,
@@ -742,6 +742,8 @@ def set_objective(self,
 
         # Initialise threshold selection and adaptive quantile
         thresholds = np.full((rounds+1), None)
+        self._quantiles = np.full((rounds+1), None)
+        self._quantiles[0] = self.initial_quantile
 
         self.objective.update(
             dict(
@@ -779,53 +781,13 @@ def update(self, batch, batch_index):
 
                 self._set_adaptive_quantile()
 
-                if self.adaptive_quantile_value < self.q_threshold:
+                if self._quantiles[self.state['round']+1] < self.q_threshold:
                     self._populations.append(self._new_population)
                     self.state['round'] += 1
                     self._init_new_round()
 
         self._update_objective()
 
-    def _init_new_round(self):
-        round = self.state['round']
-
-        self._update_round_info(round)
-
-        # Get a subseed for this round for ensuring consistent results for the round
-        seed = self.seed if round == 0 else get_sub_seed(self.seed, round)
-        self._round_random_state = np.random.RandomState(seed)
-        self._rejection = Rejection(
-            self.model,
-            discrepancy_name=self.discrepancy_name,
-            output_names=self.output_names,
-            batch_size=self.batch_size,
-            seed=seed,
-            max_parallel_batches=self.max_parallel_batches)
-
-        if self.state['round'] == 0:
-            self._rejection.set_objective(
-                self.objective['n_samples'], quantile=self.initial_quantile)
-        else:
-            self._rejection.set_objective(
-                self.objective['n_samples'],
-                threshold=self.current_population_threshold)
-
-    @property
-    def current_population_threshold(self):
-        """Return the threshold for current population."""
-        if self.state['round'] > 0:
-            self._set_threshold()
-        return self.objective['thresholds'][self.state['round']]
-
-    def _set_threshold(self):
-        """Set current population threshold as previous population quantile."""
-        threshold = weighted_sample_quantile(
-            x=self._populations[self.state['round']-1].discrepancies,
-            alpha=self.adaptive_quantile_value,
-            weights=self._populations[self.state['round']-1].weights)
-        logger.info('ABC-SMC: Selected threshold for next population %.3f' % (threshold))
-        self.objective['thresholds'][self.state['round']] = threshold
-
     def _set_adaptive_quantile(self):
         """Set adaptively the new threshold for current population."""
         logger.info("ABC-SMC: Adapting quantile threshold...")
@@ -847,8 +809,7 @@ def _set_adaptive_quantile(self):
 
         max_value = self.densratio.max_ratio()
         max_value = 1.0 if max_value < 1.0 else max_value
-        self.adaptive_quantile_value = max(1 / max_value, 0.05)
-
+        self._quantiles[self.state['round']+1] = max(1 / max_value, 0.05)
         logger.info('ABC-SMC: Estimated maximum density ratio %.5f' % (max_value))
 
     def _resolve_sample(self, backwards_index):