diff --git a/vowpalwabbit/core/include/vw/core/reductions/epsilon_decay.h b/vowpalwabbit/core/include/vw/core/reductions/epsilon_decay.h
index 23c3c6b2e93..4accadb59f1 100644
--- a/vowpalwabbit/core/include/vw/core/reductions/epsilon_decay.h
+++ b/vowpalwabbit/core/include/vw/core/reductions/epsilon_decay.h
@@ -27,10 +27,9 @@ class epsilon_decay_data
 {
 public:
   epsilon_decay_data(uint64_t model_count, uint64_t min_scope, double epsilon_decay_significance_level,
-      dense_parameters& weights, std::string epsilon_decay_audit_str,
-      bool constant_epsilon, uint32_t& feature_width, uint64_t _min_champ_examples, float initial_epsilon,
-      uint64_t shift_model_bounds, bool reward_as_cost, double tol_x, bool is_brentq, bool predict_only_model,
-      bool challenger_epsilon);
+      dense_parameters& weights, std::string epsilon_decay_audit_str, bool constant_epsilon, uint32_t& feature_width,
+      uint64_t _min_champ_examples, float initial_epsilon, uint64_t shift_model_bounds, bool reward_as_cost,
+      double tol_x, bool is_brentq, bool predict_only_model, bool challenger_epsilon);
   void update_weights(float init_ep, VW::LEARNER::learner& base, VW::multi_ex& examples);
   void promote_model(int64_t model_ind, int64_t swap_dist);
   void rebalance_greater_models(int64_t model_ind, int64_t swap_dist, int64_t model_count);
diff --git a/vowpalwabbit/core/src/reductions/epsilon_decay.cc b/vowpalwabbit/core/src/reductions/epsilon_decay.cc
index eba3618d7fc..4c631978d2c 100644
--- a/vowpalwabbit/core/src/reductions/epsilon_decay.cc
+++ b/vowpalwabbit/core/src/reductions/epsilon_decay.cc
@@ -36,10 +36,10 @@ float decayed_epsilon(float init_ep, uint64_t update_count)
 }
 
 epsilon_decay_data::epsilon_decay_data(uint64_t model_count, uint64_t min_scope,
-    double epsilon_decay_significance_level, dense_parameters& weights,
-    std::string epsilon_decay_audit_str, bool constant_epsilon, uint32_t& feature_width, uint64_t min_champ_examples,
-    float initial_epsilon, uint64_t shift_model_bounds, bool reward_as_cost, double tol_x, bool is_brentq,
-    bool predict_only_model, bool challenger_epsilon)
+    double epsilon_decay_significance_level, dense_parameters& weights, std::string epsilon_decay_audit_str,
+    bool constant_epsilon, uint32_t& feature_width, uint64_t min_champ_examples, float initial_epsilon,
+    uint64_t shift_model_bounds, bool reward_as_cost, double tol_x, bool is_brentq, bool predict_only_model,
+    bool challenger_epsilon)
     : _model_count(model_count)
     , _min_scope(min_scope)
     , _epsilon_decay_significance_level(epsilon_decay_significance_level)
@@ -437,9 +437,9 @@ std::shared_ptr<VW::LEARNER::learner> VW::reductions::epsilon_decay_setup(VW::se
   bool is_brentq = opt_func == "brentq";
 
   auto data = VW::make_unique<VW::reductions::epsilon_decay::epsilon_decay_data>(model_count, min_scope,
-      epsilon_decay_significance_level, all.weights.dense_weights,
-      epsilon_decay_audit_str, constant_epsilon, all.reduction_state.total_feature_width, min_champ_examples,
-      initial_epsilon, shift_model_bounds, reward_as_cost, tol_x, is_brentq, predict_only_model, challenger_epsilon);
+      epsilon_decay_significance_level, all.weights.dense_weights, epsilon_decay_audit_str, constant_epsilon,
+      all.reduction_state.total_feature_width, min_champ_examples, initial_epsilon, shift_model_bounds, reward_as_cost,
+      tol_x, is_brentq, predict_only_model, challenger_epsilon);
 
   // make sure we setup the rest of the stack with cleared interactions
   // to make sure there are not subtle bugs