diff --git a/agent/experiments/exp15_gentrack_from_mountain.py b/agent/experiments/exp15_gentrack_from_mountain.py
new file mode 100644
index 0000000..7f12868
--- /dev/null
+++ b/agent/experiments/exp15_gentrack_from_mountain.py
@@ -0,0 +1,186 @@
+"""
+Exp 15: Warm-start generated_track from the best mountain champion.
+
+Goal:
+- Test cross-track transfer cleanly using a single-track setup.
+- Warm-start from mountain robust winner:
+  agent/models/exp14-mountain-v5-finetune/best_robust_model_0036000.zip
+- Train on generated_track only using the known-good Exp 13 v4 setup.
+
+Why:
+- Earlier warm-start conclusions were contaminated by bad multi-track / scene
+  switching setups.
+- This isolates transfer: one source model, one target track, one stable env.
+"""
+import sys, os, time
+sys.path.insert(0, '/home/paulh/projects/donkeycar-rl-autoresearch/agent')
+
+from donkeycar_sb3_runner import ThrottleClampWrapper
+from stable_baselines3 import PPO
+from stable_baselines3.common.vec_env import DummyVecEnv, VecTransposeImage
+import gymnasium as gym
+import numpy as np
+from collections import deque
+from datetime import datetime
+
+HOST         = '10.0.0.55'
+PORT         = 9091
+TRACK_ID     = 'donkey-generated-track-v0'
+TRACK_NAME   = 'generated_track'
+THROTTLE_MIN = 0.2
+SPEED_SCALE  = 0.1
+LR           = 0.0004
+MAX_STEPS    = 300000
+EVAL_EVERY   = 5000
+LAP_STOP     = 3
+WARM_PATH    = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/models/exp14-mountain-v5-finetune/best_robust_model_0036000.zip'
+SAVE_DIR     = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/models/exp15-gentrack-from-mountain'
+os.makedirs(SAVE_DIR, exist_ok=True)
+
+class V4RewardWrapper(gym.Wrapper):
+    def __init__(self, env, speed_scale=0.1, window_size=60,
+                 min_efficiency=0.05, max_cte=8.0):
+        super().__init__(env)
+        self.speed_scale    = speed_scale
+        self.min_efficiency = min_efficiency
+        self.max_cte        = max_cte
+        self._pos_history   = deque(maxlen=window_size + 1)
+
+    def reset(self, **kwargs):
+        self._pos_history.clear()
+        return self.env.reset(**kwargs)
+
+    def step(self, action):
+        result = self.env.step(action)
+        if len(result) == 5:
+            obs, _sim_r, terminated, truncated, info = result
+            done = terminated or truncated
+        else:
+            obs, _sim_r, done, info = result
+            terminated, truncated = done, False
+        reward = self._compute_reward(done, info)
+        if len(result) == 5:
+            return obs, reward, terminated, truncated, info
+        return obs, reward, done, info
+
+    def _compute_reward(self, done, info):
+        if done:
+            return -1.0
+        pos = info.get('pos', None)
+        if pos is not None:
+            try:
+                self._pos_history.append(np.array(list(pos)[:3], dtype=np.float64))
+            except (TypeError, ValueError):
+                pass
+        try:
+            cte = float(info.get('cte', 0.0) or 0.0)
+        except (TypeError, ValueError):
+            cte = 0.0
+        base = 1.0 - min(abs(cte) / self.max_cte, 1.0)
+        efficiency = self._compute_efficiency()
+        eff = max(0.0, (efficiency - self.min_efficiency) / (1.0 - self.min_efficiency))
+        try:
+            speed = max(0.0, float(info.get('speed', 0.0) or 0.0))
+        except (TypeError, ValueError):
+            speed = 0.0
+        return base * eff * (1.0 + self.speed_scale * speed)
+
+    def _compute_efficiency(self):
+        if len(self._pos_history) < 3:
+            return 1.0
+        positions = list(self._pos_history)
+        net   = np.linalg.norm(positions[-1] - positions[0])
+        total = sum(np.linalg.norm(positions[i+1] - positions[i])
+                    for i in range(len(positions) - 1))
+        return float(net / total) if total > 1e-6 else 1.0
+
+
+def log(msg):
+    print(f'[{datetime.now().strftime("%H:%M:%S")}] {msg}', flush=True)
+
+
+def make_env():
+    def _init():
+        raw = gym.make(TRACK_ID, conf={'host': HOST, 'port': PORT})
+        env = ThrottleClampWrapper(raw, throttle_min=THROTTLE_MIN)
+        env = V4RewardWrapper(env, speed_scale=SPEED_SCALE)
+        return env
+    return _init
+
+
+log('='*60)
+log(f'Exp 15: {TRACK_NAME} warm-start from mountain robust model')
+log(f'  Host: {HOST}:{PORT}')
+log(f'  Warm start: {WARM_PATH}')
+log(f'  throttle_min={THROTTLE_MIN}, lr={LR}')
+log(f'  Reward: v4 (Exp 13 known-good generated setup)')
+log(f'  Stop: eval every {EVAL_EVERY:,} steps, stop at {LAP_STOP} laps')
+log('='*60)
+
+env = VecTransposeImage(DummyVecEnv([make_env()]))
+if os.path.exists(WARM_PATH):
+    model = PPO.load(WARM_PATH, device='cpu')
+    model.set_env(env)
+    model.learning_rate = LR
+    try:
+        for pg in model.policy.optimizer.param_groups:
+            pg['lr'] = LR
+    except Exception:
+        pass
+    log('Loaded warm-start model and attached generated_track env')
+else:
+    raise FileNotFoundError(WARM_PATH)
+
+best_reward = float('-inf')
+best_laps   = 0
+steps_done  = 0
+
+while steps_done < MAX_STEPS:
+    seg = min(EVAL_EVERY, MAX_STEPS - steps_done)
+    model.learn(total_timesteps=seg, reset_num_timesteps=False)
+    steps_done += seg
+
+    ckpt = os.path.join(SAVE_DIR, f'checkpoint_{steps_done:07d}')
+    model.save(ckpt)
+    model.save(os.path.join(SAVE_DIR, 'model'))
+
+    try:
+        obs = env.reset()
+        ep_r = 0.0
+        ep_steps = 0
+        laps = 0
+        prev_lc = 0
+        for _ in range(2000):
+            action, _ = model.predict(obs, deterministic=True)
+            obs, r, d, info = env.step(action)
+            ep_r += float(r[0])
+            ep_steps += 1
+            try:
+                lc = int((info[0] if isinstance(info, (list, tuple)) else info).get('lap_count', 0) or 0)
+                if lc > prev_lc:
+                    laps = lc
+                    prev_lc = lc
+            except Exception:
+                pass
+            if bool(d[0]):
+                break
+        status = '✅' if ep_steps >= 2000 else f'❌@{ep_steps}'
+        log(f'[{steps_done:,}] reward={ep_r:.1f} steps={ep_steps} laps={laps} {status}')
+        if ep_r > best_reward:
+            best_reward = ep_r
+            model.save(os.path.join(SAVE_DIR, 'best_model'))
+            log(f'  ⭐ NEW BEST: {best_reward:.1f}')
+        if laps > best_laps:
+            best_laps = laps
+            log(f'  🏆 BEST LAPS: {best_laps}')
+        if laps >= LAP_STOP:
+            log(f'  🎯 {laps} laps achieved at {steps_done:,} steps — STOPPING')
+            break
+    except Exception as e:
+        log(f'  Eval error: {e}')
+
+env.close()
+time.sleep(3)
+log(f'\nDone. best_laps={best_laps} best_reward={best_reward:.1f}')
+log(f'Best model: {SAVE_DIR}/best_model.zip')
+log('=== Exp 15 COMPLETE ===')
diff --git a/agent/experiments/exp16_mountain_from_gentrack.py b/agent/experiments/exp16_mountain_from_gentrack.py
new file mode 100644
index 0000000..7d0da68
--- /dev/null
+++ b/agent/experiments/exp16_mountain_from_gentrack.py
@@ -0,0 +1,194 @@
+"""
+Exp 16: Warm-start mountain_track from the generated_track champion.
+
+Goal:
+- Test reverse transfer cleanly using a single-track setup.
+- Warm-start from generated champion:
+  agent/models/exp13-gentrack-v4/best_model.zip
+- Train on mountain_track only using the known-good Exp 14 v5 setup.
+
+Caveat:
+- Mountain may still be affected by Unity traction/material issues, so results
+  should be interpreted with that in mind.
+"""
+import sys, os, time
+sys.path.insert(0, '/home/paulh/projects/donkeycar-rl-autoresearch/agent')
+
+from donkeycar_sb3_runner import ThrottleClampWrapper
+from stable_baselines3 import PPO
+from stable_baselines3.common.vec_env import DummyVecEnv, VecTransposeImage
+import gymnasium as gym
+import numpy as np
+from datetime import datetime
+
+HOST         = '10.0.0.55'
+PORT         = 9091
+TRACK_ID     = 'donkey-mountain-track-v0'
+TRACK_NAME   = 'mountain_track'
+THROTTLE_MIN = 0.2
+LR           = 0.0004
+MAX_STEPS    = 300000
+EVAL_EVERY   = 5000
+LAP_STOP     = 3
+WARM_PATH    = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/models/exp13-gentrack-v4/best_model.zip'
+SAVE_DIR     = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/models/exp16-mountain-from-gentrack'
+os.makedirs(SAVE_DIR, exist_ok=True)
+
+
+def log(msg):
+    print(f'[{datetime.now().strftime("%H:%M:%S")}] {msg}', flush=True)
+
+
+class V5RewardWrapper(gym.Wrapper):
+    def __init__(self, env, max_cte=8.0, min_lap_time=5.0):
+        super().__init__(env)
+        self.max_cte      = max_cte
+        self.min_lap_time = min_lap_time
+        self._last_lc     = 0
+
+    def reset(self, **kwargs):
+        self._last_lc = 0
+        return self.env.reset(**kwargs)
+
+    def step(self, action):
+        result = self.env.step(action)
+        if len(result) == 5:
+            obs, _r, terminated, truncated, info = result
+        else:
+            obs, _r, done, info = result
+            terminated, truncated = done, False
+        reward, force_term = self._compute(info, terminated or truncated)
+        if force_term:
+            terminated = True
+        if len(result) == 5:
+            return obs, reward, terminated, truncated, info
+        return obs, reward, terminated or truncated, info
+
+    def _compute(self, info, done):
+        if done:
+            return -1.0, False
+        try:
+            lc = int(info.get('lap_count', 0) or 0)
+        except (TypeError, ValueError):
+            lc = self._last_lc
+        if lc > self._last_lc:
+            self._last_lc = lc
+            try:
+                lt = float(info.get('last_lap_time', 999) or 999)
+            except (TypeError, ValueError):
+                lt = 999
+            if lt < self.min_lap_time:
+                penalty = -10.0 * (self.min_lap_time / max(lt, 0.1))
+                return penalty, True
+        try:
+            cte = float(info.get('cte', 0) or 0)
+        except (TypeError, ValueError):
+            cte = 0.0
+        cte_quality = 1.0 - min(abs(cte) / self.max_cte, 1.0)
+        try:
+            speed = max(0.0, float(info.get('speed', 0) or 0))
+        except (TypeError, ValueError):
+            speed = 0.0
+        speed_norm = min(speed / 10.0, 1.0)
+        return cte_quality * speed_norm, False
+
+
+def make_env():
+    def _init():
+        raw = gym.make(TRACK_ID, conf={'host': HOST, 'port': PORT})
+        env = ThrottleClampWrapper(raw, throttle_min=THROTTLE_MIN)
+        env = V5RewardWrapper(env)
+        return env
+    return _init
+
+
+log('='*60)
+log(f'Exp 16: {TRACK_NAME} warm-start from generated champion')
+log(f'  Host: {HOST}:{PORT}')
+log(f'  Warm start: {WARM_PATH}')
+log(f'  throttle_min={THROTTLE_MIN}, lr={LR}')
+log(f'  Reward: v5 (Exp 14 known-good mountain setup)')
+log(f'  Stop: eval every {EVAL_EVERY:,} steps, stop at {LAP_STOP} laps')
+log('='*60)
+
+# scene switch first
+log('Switching sim to mountain_track...')
+_tmp = gym.make('donkey-generated-track-v0', conf={'host': HOST, 'port': PORT})
+time.sleep(2)
+try:
+    _tmp.unwrapped.viewer.exit_scene()
+    time.sleep(0.5)
+except Exception as e:
+    log(f'  exit_scene warning: {e}')
+_tmp.close()
+time.sleep(6)
+log('Sim should now be at main menu. Connecting to mountain_track...')
+
+env = VecTransposeImage(DummyVecEnv([make_env()]))
+if os.path.exists(WARM_PATH):
+    model = PPO.load(WARM_PATH, device='cpu')
+    model.set_env(env)
+    model.learning_rate = LR
+    try:
+        for pg in model.policy.optimizer.param_groups:
+            pg['lr'] = LR
+    except Exception:
+        pass
+    log('Loaded warm-start model and attached mountain env')
+else:
+    raise FileNotFoundError(WARM_PATH)
+
+best_reward = float('-inf')
+best_laps   = 0
+steps_done  = 0
+
+while steps_done < MAX_STEPS:
+    seg = min(EVAL_EVERY, MAX_STEPS - steps_done)
+    model.learn(total_timesteps=seg, reset_num_timesteps=False)
+    steps_done += seg
+
+    ckpt = os.path.join(SAVE_DIR, f'checkpoint_{steps_done:07d}')
+    model.save(ckpt)
+    model.save(os.path.join(SAVE_DIR, 'model'))
+
+    try:
+        obs     = env.reset()
+        ep_r    = 0.0
+        ep_s    = 0
+        laps    = 0
+        prev_lc = 0
+        for _ in range(2000):
+            action, _ = model.predict(obs, deterministic=True)
+            obs, r, d, info = env.step(action)
+            ep_r += float(r[0])
+            ep_s += 1
+            try:
+                lc = int((info[0] if isinstance(info, (list,tuple)) else info).get('lap_count', 0) or 0)
+                if lc > prev_lc:
+                    laps = lc
+                    prev_lc = lc
+            except Exception:
+                pass
+            if bool(d[0]):
+                break
+        status = '✅' if ep_s >= 2000 else f'❌@{ep_s}'
+        log(f'[{steps_done:,}] reward={ep_r:.1f} steps={ep_s} laps={laps} {status}')
+        if ep_r > best_reward:
+            best_reward = ep_r
+            model.save(os.path.join(SAVE_DIR, 'best_model'))
+            log(f'  ⭐ NEW BEST: {best_reward:.1f}')
+        if laps > best_laps:
+            best_laps = laps
+            log(f'  🏆 BEST LAPS: {best_laps}')
+        if laps >= LAP_STOP:
+            log(f'  🎯 {laps} laps at {steps_done:,} steps — STOPPING')
+            break
+    except Exception as e:
+        log(f'  Eval error: {e}')
+        import traceback; traceback.print_exc()
+
+env.close()
+time.sleep(3)
+log(f'\nDone. best_laps={best_laps} best_reward={best_reward:.1f}')
+log(f'Best model: {SAVE_DIR}/best_model.zip')
+log('=== Exp 16 COMPLETE ===')