fix: keep async rollouts version-consistent

- serialize shared-model optimizer updates with generation
- reject future or over-lagged rollout results after asynchronous scoring
- close cache publication races
- persist policy versions in online checkpoints

tests/trainer/test_online_e2e.py

@@ -10,6 +10,7 @@ from torch.utils.data import Dataset
 import astrai.trainer.train_context as train_context
 from astrai.config import TrainConfig
 from astrai.model.transformer import AutoRegressiveLM
+from astrai.serialization import Checkpoint
 from astrai.trainer.rollout import BaseRewardModel
 from astrai.trainer.schedule import SchedulerFactory
 from astrai.trainer.trainer import Trainer
@@ -126,6 +127,7 @@ def test_online_rollout_end_to_end(
         parallel_mode="none",
         strategy_kwargs=strategy_kwargs,
         rollout_interval=1,
+        rollout_max_policy_lag=0,
         rollout_temperature=1.0,
         rollout_top_k=0,
         rollout_top_p=1.0,
@@ -137,5 +139,8 @@ def test_online_rollout_end_to_end(
     trainer = Trainer(train_config)
     trainer.train(param_path=test_dir)
 
-    assert os.path.isdir(os.path.join(test_dir, "ckpt"))
+    checkpoint_dir = os.path.join(test_dir, "ckpt", "epoch_0_step_2")
+    assert os.path.isdir(checkpoint_dir)
+    checkpoint = Checkpoint.load(checkpoint_dir)
+    assert checkpoint.meta["policy_version"] == 2
     assert len(created_reference_models) == 1