diff --git a/BayesianKDEy/_bayeisan_kdey.py b/BayesianKDEy/_bayeisan_kdey.py
index 3a202d7..8fb7d64 100644
--- a/BayesianKDEy/_bayeisan_kdey.py
+++ b/BayesianKDEy/_bayeisan_kdey.py
@@ -166,7 +166,8 @@ class BayesianKDEy(AggregativeSoftQuantifier, KDEBase, WithConfidenceABC):
             samples.append(current_prev)
             acceptance_history.append(1. if accepted else 0.)
 
-            if i < self.num_warmup and i%10==0 and len(acceptance_history)>=100:
+            # if i < self.num_warmup and i%10==0 and len(acceptance_history)>=100:
+            if i % 10 == 0 and len(acceptance_history) >= 100:
                 recent_accept_rate = np.mean(acceptance_history[-100:])
                 step_size *= np.exp(adapt_rate * (recent_accept_rate - target_acceptance))
                 # step_size = float(np.clip(step_size, min_step, max_step))
diff --git a/BayesianKDEy/single_experiment_debug.py b/BayesianKDEy/single_experiment_debug.py
index e9b71b5..ca929eb 100644
--- a/BayesianKDEy/single_experiment_debug.py
+++ b/BayesianKDEy/single_experiment_debug.py
@@ -47,8 +47,8 @@ def methods():
 
     # yield 'BootstrapKDEy', KDEyML(LR()), kdey_hyper, lambda hyper: AggregativeBootstrap(KDEyML(LR(), **hyper), n_test_samples=1000, random_state=0, verbose=True),
     # yield 'BayesianKDEy', KDEyML(LR()), kdey_hyper, lambda hyper: BayesianKDEy(mcmc_seed=0, **hyper),
-    for T in [1., 10., 100., 1000.]:
-        yield f'BaKDE-CLR-T{T}', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, temperature=T, step_size=.15, **hyper),
+    for T in [100., 500, 1000.]:
+        yield f'BaKDE-CLR-T{T}', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', explore='ilr', mcmc_seed=0, temperature=T, step_size=.1, **hyper),