diff --git a/miplearn/__init__.py b/miplearn/__init__.py
index 3590434..c1240b6 100644
--- a/miplearn/__init__.py
+++ b/miplearn/__init__.py
@@ -6,4 +6,6 @@
 from .component import Component
 from .instance import Instance
 from .solvers import LearningSolver
-from .benchmark import BenchmarkRunner
\ No newline at end of file
+from .benchmark import BenchmarkRunner
+from .warmstart import WarmStartComponent
+from .branching import BranchPriorityComponent
\ No newline at end of file
diff --git a/miplearn/problems/stab.py b/miplearn/problems/stab.py
index 61f93f8..0ae6183 100644
--- a/miplearn/problems/stab.py
+++ b/miplearn/problems/stab.py
@@ -14,22 +14,22 @@ from scipy.stats.distributions import rv_frozen
 class MaxWeightStableSetChallengeA:
     """
     - Fixed random graph (200 vertices, 5% density)
-    - Uniformly random weights in the [100., 125.] interval
-    - 500 training instances
-    - 100 test instances
+    - Random weights ~ U(100., 150.)
+    - 300 training instances
+    - 50 test instances
     """
     
     def __init__(self):
-        self.generator = MaxWeightStableSetGenerator(w=uniform(loc=100., scale=25.),
+        self.generator = MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.),
                                                      n=randint(low=200, high=201),
                                                      density=uniform(loc=0.05, scale=0.0),
                                                      fix_graph=True)
     
     def get_training_instances(self):
-        return self.generator.generate(500)
+        return self.generator.generate(300)
     
     def get_test_instances(self):
-        return self.generator.generate(100)
+        return self.generator.generate(50)
 
 
 class MaxWeightStableSetGenerator:
diff --git a/miplearn/scripts/branchpriority.jl b/miplearn/scripts/branchpriority.jl
index 99d9174..69aadc8 100644
--- a/miplearn/scripts/branchpriority.jl
+++ b/miplearn/scripts/branchpriority.jl
@@ -51,7 +51,7 @@ branch_and_bound(mip,
                  node_limit = 1000,
                  branch_rule = full_strong_branching_track,
                  node_rule = best_bound,
-                 print_interval = 1)
+                 print_interval = 100)
 
 priority = [(pseudocost_count_up[v] == 0 || pseudocost_count_down[v] == 0) ? 0 :
               (pseudocost_sum_up[v] / pseudocost_count_up[v]) *
diff --git a/miplearn/solvers.py b/miplearn/solvers.py
index 670febc..e9785bc 100644
--- a/miplearn/solvers.py
+++ b/miplearn/solvers.py
@@ -48,7 +48,7 @@ class LearningSolver:
             
         if mode is not None:
             assert mode in ["exact", "heuristic"]
-            for component in self.components:
+            for component in self.components.values():
                 component.mode = mode
         
     def _create_solver(self):
diff --git a/miplearn/warmstart.py b/miplearn/warmstart.py
index 757d8d2..9e003c8 100644
--- a/miplearn/warmstart.py
+++ b/miplearn/warmstart.py
@@ -50,7 +50,7 @@ class WarmStartPredictor(ABC):
 class LogisticWarmStartPredictor(WarmStartPredictor):
     def __init__(self,
                  min_samples=100,
-                 thr_fix=[0.99, 0.99],
+                 thr_fix=[0.95, 0.95],
                  thr_balance=[0.95, 0.95],
                  thr_score=[0.95, 0.95]):
         super().__init__()
@@ -151,8 +151,12 @@ class WarmStartComponent(Component):
                     if self.mode == "heuristic":
                         if ws[i,0] == 1:
                             var[index].fix(0)
+                            if solver.is_persistent:
+                                solver.internal_solver.update_var(var[index])
                         elif ws[i,1] == 1:
                             var[index].fix(1)
+                            if solver.is_persistent:
+                                solver.internal_solver.update_var(var[index])
                     else:
                         if ws[i,0] == 1:
                             var[index].value = 0