feat: version rollout weight updates

Track a monotonic policy version across optimizer steps, scheduler updates, and rollout results. Serialize synchronous generation with weight acknowledgements and invalidate reusable prefix KV entries so cached samples remain attributable to the behavior policy that generated them.

astrai/inference/scheduler.py

@@ -2,6 +2,7 @@ import logging
 import threading
 import uuid
 from contextlib import nullcontext
+from functools import wraps
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
@@ -28,6 +29,15 @@ from astrai.tokenize.tokenizer import AutoTokenizer
 logger = logging.getLogger(__name__)
 
 
+def _with_weight_lock(method):
+    @wraps(method)
+    def synchronized(self, *args, **kwargs):
+        with self._weight_lock:
+            return method(self, *args, **kwargs)
+
+    return synchronized
+
+
 class InferenceScheduler:
     """Continuous batching loop: cleanup -> refill -> prefill -> decode (all groups)."""
 
@@ -42,7 +52,14 @@ class InferenceScheduler:
         cache: Optional[PagePool] = None,
         enable_cuda_graph: bool = True,
         backend: Optional[Union[str, ATTN_BACKEND, AttentionBackend, type]] = None,
+        policy_version: int = 0,
     ):
+        if (
+            isinstance(policy_version, bool)
+            or not isinstance(policy_version, int)
+            or policy_version < 0
+        ):
+            raise ValueError("policy_version must be a non-negative integer")
         config = model.config
 
         if max_seq_len is not None:
@@ -103,6 +120,44 @@ class InferenceScheduler:
 
         self._stop_event = threading.Event()
         self._loop_thread: Optional[threading.Thread] = None
+        self._weight_lock = threading.RLock()
+        self._policy_version = policy_version
+
+    @property
+    def policy_version(self) -> int:
+        """Version of the model weights used for subsequent generations."""
+        return self._policy_version
+
+    @_with_weight_lock
+    def update_weights(self, policy_version: int) -> int:
+        """Acknowledge an in-place weight update and invalidate stale KV state.
+
+        The scheduler owns the same model object as the in-process trainer, so
+        weights have already changed when this method is called.  The explicit
+        version update makes that lifecycle visible and prevents prefix KV
+        entries produced by older weights from being reused.
+        """
+        if (
+            isinstance(policy_version, bool)
+            or not isinstance(policy_version, int)
+            or policy_version < 0
+        ):
+            raise ValueError("policy_version must be a non-negative integer")
+        if policy_version < self._policy_version:
+            raise ValueError(
+                f"policy_version cannot move backwards from "
+                f"{self._policy_version} to {policy_version}"
+            )
+        if policy_version == self._policy_version:
+            return self._policy_version
+        if self._loop_thread is not None and self._loop_thread.is_alive():
+            raise RuntimeError("Stop the scheduler before updating model weights")
+        if self._task_mgr.get_active_tasks() or self._task_mgr.get_waiting_tasks():
+            raise RuntimeError("Cannot update model weights while tasks are queued")
+
+        self._task_cache.invalidate_cache()
+        self._policy_version = policy_version
+        return self._policy_version
 
     def add_task(self, prompt: str, **kwargs) -> str:
         return self._task_mgr.add_task(prompt, **kwargs)
@@ -125,6 +180,7 @@ class InferenceScheduler:
     def get_stats(self) -> Dict[str, Any]:
         stats = self._task_mgr.get_stats()
         stats["kv_cache_tasks"] = self._task_cache.task_count
+        stats["policy_version"] = self._policy_version
         return stats
 
     @property
@@ -343,6 +399,7 @@ class InferenceScheduler:
             )
         self._task_mgr.clear_queues()
 
+    @_with_weight_lock
     def run_batch(
         self,
         prompt_ids_list: List[List[int]],