diff --git a/agent/experiments/exp14_finetune_v5.py b/agent/experiments/exp14_finetune_v5.py
index 6fc6b13..9a4bdce 100644
--- a/agent/experiments/exp14_finetune_v5.py
+++ b/agent/experiments/exp14_finetune_v5.py
@@ -16,6 +16,7 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
 from datetime import datetime
 from stable_baselines3 import PPO
 from stable_baselines3.common.vec_env import DummyVecEnv, VecTransposeImage
+from stable_baselines3.common.utils import get_schedule_fn
 import gymnasium as gym, numpy as np
 from donkeycar_sb3_runner import ThrottleClampWrapper
 
@@ -163,11 +164,18 @@ loaded_env = VecTransposeImage(DummyVecEnv([make_env_base(0.2, throttle_floor=No
 if os.path.exists(WARM_PATH):
     log(f'Loading warm-start model from {WARM_PATH} using base throttle_min=0.2 env')
     model = PPO.load(WARM_PATH, env=loaded_env, device='cpu')
-    # override lr and schedules
+    # override lr and schedules — ensure lr_schedule callable exists
     model.learning_rate = LR
-    model.lr_schedule = model.get_schedule_fn(LR) if hasattr(model,'get_schedule_fn') else None
-    for pg in getattr(getattr(model.policy,'optimizer',None) or [], 'param_groups', []):
-        pg['lr'] = LR
+    try:
+        model.lr_schedule = get_schedule_fn(LR)
+    except Exception:
+        model.lr_schedule = None
+    # update optimizer param groups to new LR
+    try:
+        for pg in model.policy.optimizer.param_groups:
+            pg['lr'] = LR
+    except Exception:
+        pass
     # Create the training env using base action space but enforce throttle_floor at runtime
     first_throttle_floor = phase_defs[0][1]
     env0 = VecTransposeImage(DummyVecEnv([make_env_base(0.2, throttle_floor=first_throttle_floor)]))