继续做了通用性能优化，重点从 jepa.py 热路径转到实际的 stable_worldmodel

solver/policy 边界：去掉 CEM 每轮 cpu().tolist() 和结果过早回 CPU，把
  plan/warm-start 保持在 GPU，只在 env.step 前最后一步转成 numpy，同时补
  了输入张量的 contiguous 处理；

.venv/lib/python3.10/site-packages/stable_worldmodel/policy.py

@@ -122,8 +122,12 @@ class BasePolicy:
     ) -> dict[str, Any]:
         target = torch.device(device)
         for k, v in info_dict.items():
-            if torch.is_tensor(v) and v.device != target:
-                info_dict[k] = v.to(target, non_blocking=True)
+            if torch.is_tensor(v):
+                if v.device != target:
+                    v = v.to(target, non_blocking=True)
+                if not v.is_contiguous():
+                    v = v.contiguous()
+                info_dict[k] = v
         return info_dict
 
     def _prepare_info(self, info_dict: dict) -> dict[str, torch.Tensor]:
@@ -415,18 +419,21 @@ class WorldModelPolicy(BasePolicy):
             keep_horizon = self.cfg.receding_horizon
             plan = actions[:, :keep_horizon]
             rest = actions[:, keep_horizon:]
-            self._next_init = rest if self.cfg.warm_start else None
+            self._next_init = rest.contiguous() if self.cfg.warm_start else None
 
             # frameskip back to timestep
             plan = plan.reshape(
                 self.env.num_envs, self.flatten_receding_horizon, -1
-            )
+            ).contiguous()
 
-            self._action_buffer.extend(plan.transpose(0, 1))
+            self._action_buffer.extend(plan.transpose(0, 1).unbind(0))
 
         action = self._action_buffer.popleft()
         action = action.reshape(*self.env.action_space.shape)
-        action = action.numpy()
+        if torch.is_tensor(action):
+            action = action.detach().cpu().numpy()
+        else:
+            action = np.asarray(action)
 
         # post-process action
         if 'action' in self.process: