diff --git a/pypesto/optimize/ess/ess.py b/pypesto/optimize/ess/ess.py
index a9cad7b36..ca8ffa2f6 100644
--- a/pypesto/optimize/ess/ess.py
+++ b/pypesto/optimize/ess/ess.py
@@ -564,7 +564,7 @@ def _maybe_update_global_best(self, x, fx):
             self.fx_best = fx
             self.x_best_has_changed = True
             self.history.update(
-                self.x_best,
+                self.x_best.copy(),
                 (0,),
                 pypesto.C.MODE_FUN,
                 {pypesto.C.FVAL: self.fx_best},
diff --git a/pypesto/optimize/ess/refset.py b/pypesto/optimize/ess/refset.py
index 74f5c8f6b..0e3cff403 100644
--- a/pypesto/optimize/ess/refset.py
+++ b/pypesto/optimize/ess/refset.py
@@ -67,6 +67,14 @@ def __init__(
         self.n_stuck = np.zeros(shape=[dim])
         self.attributes: dict[Any, np.array] = {}
 
+    def __repr__(self):
+        fx = (
+            f", fx=[{np.min(self.fx)} ... {np.max(self.fx)}]"
+            if self.fx is not None and len(self.fx) >= 2
+            else ""
+        )
+        return f"RefSet(dim={self.dim}{fx})"
+
     def sort(self):
         """Sort RefSet by quality."""
         order = np.argsort(self.fx)
diff --git a/pypesto/optimize/ess/sacess.py b/pypesto/optimize/ess/sacess.py
index 775d5458f..c54ef0ad6 100644
--- a/pypesto/optimize/ess/sacess.py
+++ b/pypesto/optimize/ess/sacess.py
@@ -195,7 +195,7 @@ def minimize(
         start_time = time.time()
         logger.debug(
             f"Running {self.__class__.__name__} with {self.num_workers} "
-            f"workers: {self.ess_init_args}"
+            f"workers: {self.ess_init_args} and {self.options}."
         )
         ess_init_args = self.ess_init_args or get_default_ess_options(
             num_workers=self.num_workers, dim=problem.dim
@@ -563,7 +563,8 @@ def run(
         self._manager._logger = self._logger
 
         self._logger.debug(
-            f"#{self._worker_idx} starting " f"({self._ess_kwargs})."
+            f"#{self._worker_idx} starting "
+            f"({self._ess_kwargs}, {self._options})."
         )
 
         evaluator = create_function_evaluator(
@@ -694,6 +695,13 @@ def _maybe_adapt(self, problem: Problem):
                 f"Updated settings on worker {self._worker_idx} to "
                 f"{self._ess_kwargs}"
             )
+        else:
+            self._logger.debug(
+                f"Worker {self._worker_idx} not adapting. "
+                f"Received: {self._n_received_solutions} <= {self._options.adaptation_sent_coeff * self._n_sent_solutions + self._options.adaptation_sent_offset}, "
+                f"Sent: {self._n_sent_solutions}, "
+                f"neval: {self._neval} <= {problem.dim * self._options.adaptation_min_evals}."
+            )
 
     def maybe_update_best(self, x: np.array, fx: float):
         """Maybe update the best known solution and send it to the manager."""
@@ -840,13 +848,6 @@ def dim_refset(x):
         return max(min_dimrefset, ceil((1 + sqrt(4 * dim * x)) / 2))
 
     settings = [
-        # settings for first worker
-        {
-            "dim_refset": dim_refset(10),
-            "balance": 0.5,
-            "local_n2": 10,
-        },
-        # for the remaining workers, cycle through these settings
         # 1
         {
             "dim_refset": dim_refset(1),
@@ -998,10 +999,7 @@ def dim_refset(x):
         elif local_optimizer is not False:
             cur_settings["local_optimizer"] = local_optimizer
 
-    return [
-        settings[0],
-        *(itertools.islice(itertools.cycle(settings[1:]), num_workers - 1)),
-    ]
+    return list(itertools.islice(itertools.cycle(settings), num_workers))
 
 
 class SacessFidesFactory:
diff --git a/test/optimize/test_optimize.py b/test/optimize/test_optimize.py
index 160e5baa9..48ebdea55 100644
--- a/test/optimize/test_optimize.py
+++ b/test/optimize/test_optimize.py
@@ -20,8 +20,11 @@
 import pypesto.optimize as optimize
 from pypesto.optimize.ess import (
     ESSOptimizer,
+    FunctionEvaluatorMP,
+    RefSet,
     SacessFidesFactory,
     SacessOptimizer,
+    SacessOptions,
     get_default_ess_options,
 )
 from pypesto.optimize.util import (
@@ -490,6 +493,11 @@ def test_ess(problem, local_optimizer, ess_type, request):
             sacess_loglevel=logging.DEBUG,
             ess_loglevel=logging.WARNING,
             ess_init_args=ess_init_args,
+            options=SacessOptions(
+                adaptation_min_evals=500,
+                adaptation_sent_offset=10,
+                adaptation_sent_coeff=5,
+            ),
         )
     else:
         raise ValueError(f"Unsupported ESS type {ess_type}.")
@@ -522,8 +530,6 @@ def test_ess_multiprocess(problem, request):
 
     from fides.constants import Options as FidesOptions
 
-    from pypesto.optimize.ess import ESSOptimizer, FunctionEvaluatorMP, RefSet
-
     # augment objective with parameter prior to check it's copyable
     #  https://github.com/ICB-DCM/pyPESTO/issues/1465
     #  https://github.com/ICB-DCM/pyPESTO/pull/1467
@@ -563,6 +569,14 @@ def test_ess_multiprocess(problem, request):
     print("ESS result: ", res.summary())
 
 
+def test_ess_refset_repr():
+    assert RefSet(10, None).__repr__() == "RefSet(dim=10)"
+    assert (
+        RefSet(10, None, x=np.zeros(10), fx=np.arange(10)).__repr__()
+        == "RefSet(dim=10, fx=[0 ... 9])"
+    )
+
+
 def test_scipy_integrated_grad():
     integrated = True
     obj = rosen_for_sensi(max_sensi_order=2, integrated=integrated)["obj"]