diff --git a/src/aspire/samplers/smc/base.py b/src/aspire/samplers/smc/base.py
index 7e713db..49b295b 100644
--- a/src/aspire/samplers/smc/base.py
+++ b/src/aspire/samplers/smc/base.py
@@ -20,6 +20,12 @@
 
 logger = logging.getLogger(__name__)
 
+DEFAULT_BETA_TOLERANCE = 1e-8
+
+
+class BetaScheduleError(RuntimeError):
+    pass
+
 
 class SMCSampler(MCMCSampler):
     """Base class for Sequential Monte Carlo samplers.
@@ -121,7 +127,7 @@ def determine_beta(
         beta_step: float,
         min_beta_step: float,
         max_beta_step: float = 1.0,
-        beta_tolerance: float = 1e-6,
+        beta_tolerance: float = DEFAULT_BETA_TOLERANCE,
     ) -> tuple[float, float]:
         """Determine the next beta value.
 
@@ -146,6 +152,12 @@ def determine_beta(
             The new beta value.
         min_beta_step : float
             The new minimum beta step size if adaptive_min_beta_step is True.
+
+        Raises
+        ------
+        BetaScheduleError
+            If adaptive beta is enabled and the determined beta does not increase
+            from the previous beta.
         """
         if not self.adaptive:
             beta += beta_step
@@ -158,9 +170,10 @@ def determine_beta(
             eff_beta_max = effective_sample_size(
                 samples.log_weights(beta_max)
             ) / len(samples)
-            if eff_beta_max >= self.current_target_efficiency(beta_prev):
+            current_eff = self.current_target_efficiency(beta_prev)
+            if eff_beta_max >= current_eff:
                 beta_min = 1.0
-            target_eff = self.current_target_efficiency(beta_prev)
+            target_eff = current_eff
             while beta_max - beta_min > beta_tolerance:
                 beta_try = 0.5 * (beta_max + beta_min)
                 eff = effective_sample_size(
@@ -171,6 +184,16 @@ def determine_beta(
                 else:
                     beta_max = beta_try
             beta_star = beta_min
+            if beta_star <= beta_prev + beta_tolerance and beta_prev < 1.0:
+                logger.warning(
+                    "Adaptive beta search could not find a beta above %.6g "
+                    "that satisfies the target efficiency %.3f within "
+                    "tolerance %.1e; beta may remain unchanged. "
+                    "Consider decreasing beta_tolerance or target_efficiency.",
+                    beta_prev,
+                    target_eff,
+                    beta_tolerance,
+                )
 
             if self.adaptive_min_beta_step:
                 min_beta_step = (
@@ -178,6 +201,15 @@ def determine_beta(
                 )
             beta = max(beta_star, beta_prev + min_beta_step)
             beta = min(beta, beta_prev + max_beta_step, 1.0)
+            if beta == beta_prev:
+                raise BetaScheduleError(
+                    f"Beta did not increase from previous value {beta:.6g}. "
+                    "Adaptive beta search may have failed to find a suitable beta. "
+                    f"Consider adjusting beta_tolerance ({beta_tolerance}), "
+                    f"min_beta_step ({min_beta_step}) or "
+                    f"target_efficiency ({target_eff}) "
+                    "(values may be adaptive)."
+                )
         return beta, min_beta_step
 
     @track_calls
@@ -197,7 +229,7 @@ def sample(
         checkpoint_file_path: str | None = None,
         resume_from: str | bytes | dict | None = None,
         store_sample_history: bool = True,
-        beta_tolerance: float = 1e-6,
+        beta_tolerance: float = DEFAULT_BETA_TOLERANCE,
     ) -> SMCSamples:
         """Sample using the SMC sampler.
 
@@ -250,7 +282,7 @@ def sample(
             :code:`self.history.sample_history`. Default is True.
         beta_tolerance : float, optional
             Tolerance for determining convergence of beta when using adaptive
-            beta. Default is 1e-6.
+            beta. Default is given by :code:`DEFAULT_BETA_TOLERANCE`.
 
         Returns
         -------