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/trainer/rollout.py

@@ -13,6 +13,7 @@ Provides:
   so callers do not need to rely on object identity to detect refreshes.
 """
 
+import threading
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 from typing import Dict, List, Optional, Tuple
@@ -53,6 +54,7 @@ class RawRollout:
     responses: Tensor
     response_mask: Tensor
     logprobs_old: Tensor
+    policy_version: int = 0
     prompt_texts: List[str] = field(default_factory=list)
     response_texts: List[List[str]] = field(default_factory=list)
 
@@ -129,6 +131,16 @@ class RolloutGenerator:
         self.top_p = top_p
         self.frequency_penalty = frequency_penalty
         self.rep_window = rep_window
+        self._weight_lock = threading.RLock()
+
+    @property
+    def policy_version(self) -> int:
+        return self.scheduler.policy_version
+
+    def update_weights(self, policy_version: int) -> int:
+        """Acknowledge shared-model weights and invalidate older scheduler KV."""
+        with self._weight_lock:
+            return self.scheduler.update_weights(policy_version)
 
     @torch.no_grad()
     def generate(self, batch: Dict) -> RawRollout:
@@ -146,13 +158,14 @@ class RolloutGenerator:
         ``add_generation_prompt=True`` so rollout prompts match the
         format the policy was SFT-trained on.
         """
-        model = self.scheduler._executor.model
-        was_training = model.training
-        model.eval()
-        try:
-            return self._generate_eval(batch)
-        finally:
-            model.train(was_training)
+        with self._weight_lock:
+            model = self.scheduler._executor.model
+            was_training = model.training
+            model.eval()
+            try:
+                return self._generate_eval(batch)
+            finally:
+                model.train(was_training)
 
     def _generate_eval(self, batch: Dict) -> RawRollout:
         prompt_texts, flat_prompt_ids = self._prepare_prompts(batch)
@@ -245,6 +258,7 @@ class RolloutGenerator:
             responses=responses,
             response_mask=response_mask,
             logprobs_old=logprobs_old,
+            policy_version=self.policy_version,
             prompt_texts=prompt_texts,
             response_texts=response_texts,
         )
@@ -370,6 +384,14 @@ class RolloutRunner:
         self._cache_key = None
         self._steps_since_rollout: int = 0
 
+    @property
+    def policy_version(self) -> int:
+        return self.generator.policy_version
+
+    def update_weights(self, policy_version: int) -> int:
+        """Publish the shared policy's new version to the rollout backend."""
+        return self.generator.update_weights(policy_version)
+
     def step(self):
         """Advance the internal counter (call once per optimizer step)."""
         self._steps_since_rollout += 1
@@ -415,6 +437,7 @@ class RolloutRunner:
             response_mask=raw.response_mask,
             rewards=rewards.to(device=device),
             logprobs_old=raw.logprobs_old,
+            policy_version=raw.policy_version,
             prompt_texts=raw.prompt_texts,
             response_texts=raw.response_texts,
         )