diff --git a/changelog.md b/changelog.md
index 241515e97..89e08e38f 100644
--- a/changelog.md
+++ b/changelog.md
@@ -1,5 +1,10 @@
 # Changelog
 
+## Unreleased
+
+### Added
+- New parameter `pruning_params` to `edsnlp.tune` in order to control pruning during tuning.
+
 ## v0.19.0 (2025-10-04)
 
 📢 EDS-NLP will drop support for Python 3.7, 3.8 and 3.9 support in the next major release (v0.20.0), in October 2025. Please upgrade to Python 3.10 or later.
diff --git a/edsnlp/tune.py b/edsnlp/tune.py
index 0f56e0194..381ba693a 100644
--- a/edsnlp/tune.py
+++ b/edsnlp/tune.py
@@ -271,7 +271,7 @@ def update_config(
     return config
 
 
-def objective_with_param(config, tuned_parameters, trial, metric):
+def objective_with_param(config, tuned_parameters, trial, metric, pruning_params):
     kwargs, _ = update_config(config, tuned_parameters, trial=trial)
     seed = random.randint(0, 2**32 - 1)
     set_seed(seed)
@@ -282,8 +282,9 @@ def on_validation_callback(all_metrics):
         for key in metric:
             score = score[key]
         trial.report(score, step)
-        if trial.should_prune():
-            raise optuna.TrialPruned()
+        if pruning_params:
+            if trial.should_prune():
+                raise optuna.TrialPruned()
 
     try:
         nlp = train(**kwargs, on_validation_callback=on_validation_callback)
@@ -299,15 +300,30 @@ def on_validation_callback(all_metrics):
 
 
 def optimize(
-    config_path, tuned_parameters, n_trials, metric, checkpoint_dir, study=None
+    config_path,
+    tuned_parameters,
+    n_trials,
+    metric,
+    checkpoint_dir,
+    pruning_params,
+    study=None,
 ):
     def objective(trial):
-        return objective_with_param(config_path, tuned_parameters, trial, metric)
+        return objective_with_param(
+            config_path, tuned_parameters, trial, metric, pruning_params
+        )
 
     if not study:
+        pruner = None
+        if pruning_params:
+            n_startup_trials = pruning_params.get("n_startup_trials", 5)
+            n_warmup_steps = pruning_params.get("n_warmup_steps", 5)
+            pruner = MedianPruner(
+                n_startup_trials=n_startup_trials, n_warmup_steps=n_warmup_steps
+            )
         study = optuna.create_study(
             direction="maximize",
-            pruner=MedianPruner(n_startup_trials=5, n_warmup_steps=2),
+            pruner=pruner,
             sampler=TPESampler(seed=random.randint(0, 2**32 - 1)),
         )
     study.optimize(
@@ -444,6 +460,7 @@ def tune_two_phase(
     is_fixed_n_trials: bool = False,
     gpu_hours: float = 1.0,
     skip_phase_1: bool = False,
+    pruning_params: Dict[str, int] = None,
 ) -> None:
     """
     Perform two-phase hyperparameter tuning using Optuna.
@@ -505,6 +522,7 @@ def tune_two_phase(
             n_trials_1,
             metric,
             checkpoint_dir,
+            pruning_params,
             study,
         )
         best_params_phase_1, importances = process_results(
@@ -551,6 +569,7 @@ def tune_two_phase(
         n_trials_2,
         metric,
         checkpoint_dir,
+        pruning_params,
         study,
     )
 
@@ -612,6 +631,7 @@ def tune(
     seed: int = 42,
     metric="ner.micro.f",
     keep_checkpoint: bool = False,
+    pruning_params: Optional[Dict[str, int]] = None,
 ):
     """
     Perform hyperparameter tuning for a model using Optuna.
@@ -652,6 +672,11 @@ def tune(
         Metric used to evaluate trials. Default is "ner.micro.f".
     keep_checkpoint : bool, optional
         If True, keeps the checkpoint file after tuning. Default is False.
+    pruning_params : dict, optional
+        A dictionary specifying pruning parameters:
+        - "n_startup_trials": Number of startup trials before pruning starts.
+        - "n_warmup_steps": Number of warmup steps before pruning starts.
+        Default is None, meaning no pruning.
     """
     setup_logging()
     viz = is_plotly_install()
@@ -679,6 +704,7 @@ def tune(
                 n_trials=1,
                 metric=metric,
                 checkpoint_dir=checkpoint_dir,
+                pruning_params=pruning_params,
             )
             n_trials = compute_n_trials(gpu_hours, compute_time_per_trial(study)) - 1
         else:
@@ -708,6 +734,7 @@ def tune(
             is_fixed_n_trials=is_fixed_n_trials,
             gpu_hours=gpu_hours,
             skip_phase_1=skip_phase_1,
+            pruning_params=pruning_params,
         )
     else:
         logger.info("Starting single-phase tuning.")
@@ -717,6 +744,7 @@ def tune(
             n_trials,
             metric,
             checkpoint_dir,
+            pruning_params,
             study,
         )
         if not is_fixed_n_trials:
@@ -732,6 +760,7 @@ def tune(
                     n_trials,
                     metric,
                     checkpoint_dir,
+                    pruning_params,
                     study,
                 )
         process_results(study, output_dir, viz, config, config_path, hyperparameters)