diff --git a/tensorflow_probability/python/experimental/mcmc/snaper_hmc.py b/tensorflow_probability/python/experimental/mcmc/snaper_hmc.py
index 49080e4f4a..063e43cc83 100644
--- a/tensorflow_probability/python/experimental/mcmc/snaper_hmc.py
+++ b/tensorflow_probability/python/experimental/mcmc/snaper_hmc.py
@@ -1005,26 +1005,26 @@ def outer_trace_fn(all_state):
     return unconstrained_state, kernel_results, trace
 
 
-def default_snaper_trace_fn(state, is_burnin, kernel_results, reducer,
-                            reducer_state):
+def default_snaper_trace_fn(
+    state, is_burnin, kernel_results, reducer, reducer_state
+):
+  """Default trace function for SNAPER."""
   del reducer, reducer_state
   kr = kernel_results
   energy_diff = unnest.get_innermost(kr, 'log_accept_ratio')
   # The ~ is here to catch NaNs.
-  has_divergence = ~(tf.math.abs(energy_diff) < 500.)
+  has_divergence = ~(tf.math.abs(energy_diff) < 500.0)
+  # SNAPER rescales the inner HMC kernel by max_ema_variance, so to aid
+  # comparisons with other algorithms which typically don't do this rescaling,
+  # we undo the rescaling here. This makes the step size consistent with the
+  # target_log_prob_fn scale implied by `variance_scaling` below.
+  scale = 1.0 / tf.sqrt(unnest.get_innermost(kr, 'max_ema_variance'))
   return state, {
-      # SNAPER rescales the inner HMC kernel by max_ema_variance, so to aid
-      # comparisons with other algorithms which typically don't do this
-      # rescaling, we undo the rescaling here. This makes the step size
-      # consistent with the target_log_prob_fn scale implied by
-      # `variance_scaling` below.
-      'step_size': unnest.get_innermost(kr, 'step_size') / tf.sqrt(
-          unnest.get_innermost(kr, 'max_ema_variance')
-      ),
+      'step_size': unnest.get_innermost(kr, 'step_size') * scale,
       'n_steps': unnest.get_innermost(kr, 'num_leapfrog_steps'),
       'tune': is_burnin,
-      'max_trajectory_length': unnest.get_innermost(
-          kr, 'max_trajectory_length'
+      'max_trajectory_length': (
+          unnest.get_innermost(kr, 'max_trajectory_length') * scale
       ),
       'variance_scaling': tf.nest.map_structure(
           lambda x: 1.0 / x, unnest.get_innermost(kr, 'ema_variance')
diff --git a/tensorflow_probability/python/experimental/mcmc/snaper_hmc_test.py b/tensorflow_probability/python/experimental/mcmc/snaper_hmc_test.py
index 1d367ebe37..58d7bc9fd6 100644
--- a/tensorflow_probability/python/experimental/mcmc/snaper_hmc_test.py
+++ b/tensorflow_probability/python/experimental/mcmc/snaper_hmc_test.py
@@ -106,12 +106,11 @@ def testEndToEndAdaptation(self):
     )
 
     def trace_fn(_, pkr):
+      scale = 1.0 / tf.sqrt(unnest.get_innermost(pkr, 'max_ema_variance'))
       return {
-          'step_size': unnest.get_innermost(pkr, 'step_size') / tf.sqrt(
-              unnest.get_innermost(pkr, 'max_ema_variance')
-          ),
+          'step_size': scale * unnest.get_innermost(pkr, 'step_size'),
           'mean_trajectory_length': (
-              unnest.get_innermost(pkr, 'max_trajectory_length') / 2.0
+              scale * unnest.get_innermost(pkr, 'max_trajectory_length') / 2.0
           ),
           'principal_component': unnest.get_innermost(
               pkr, 'ema_principal_component'
@@ -143,7 +142,7 @@ def trace_fn(_, pkr):
     # Adaptation results.
     # Obtained via a separate run of `windowed_adaptive_nuts`.
     self.assertAllClose(0.45, trace['step_size'][-1], rtol=0.25)
-    self.assertAllClose(4., trace['mean_trajectory_length'][-1], atol=1.)
+    self.assertAllClose(1.25, trace['mean_trajectory_length'][-1], rtol=0.3)
     self.assertAllClose(np.diag(covariance), trace['variance'][-1], rtol=0.2)
     self.assertAllClose(
         principal_component / np.sign(principal_component[0]),
@@ -312,7 +311,7 @@ def run(seed):
     self.assertEqual(self.dtype, chain.dtype)
     # Obtained via a separate run of `windowed_adaptive_nuts`.
     self.assertAllClose(0.45, trace['step_size'][-1], rtol=0.25)
-    self.assertAllClose(8., trace['max_trajectory_length'][-1], atol=2.)
+    self.assertAllClose(2.5, trace['max_trajectory_length'][-1], rtol=0.3)
     self.assertAllClose(chain.var((0, 1)), np.diag(covariance), rtol=0.2)
     self.assertAllClose(
         np.ones(num_dims, self.dtype), reduction_results, atol=0.1)