fix: correct online rollout lifecycle

astrai/dataset/dataset.py

@@ -190,7 +190,8 @@ def grpo_collate_fn(batch: List[Dict[str, Tensor]]) -> Dict[str, Tensor]:
       - rewards:  [G]
 
     Output:
-      - prompts:   [B, P_max]
+      - prompts:   [B, P_max], left-padded
+      - prompt_mask: [B, P_max]
       - responses: [B, G, R_max]
       - masks:     [B, G, R_max]
       - rewards:   [B, G]
@@ -201,13 +202,15 @@ def grpo_collate_fn(batch: List[Dict[str, Tensor]]) -> Dict[str, Tensor]:
     R_max = max(r.size(0) for b in batch for r in b["responses"])
 
     prompts = torch.zeros(B, P_max, dtype=torch.long)
+    prompt_mask = torch.zeros(B, P_max, dtype=torch.bool)
     responses = torch.zeros(B, G, R_max, dtype=torch.long)
     masks = torch.zeros(B, G, R_max, dtype=torch.bool)
     rewards = torch.zeros(B, G, dtype=torch.float32)
 
     for i, b in enumerate(batch):
         p_len = b["prompts"].size(0)
-        prompts[i, :p_len] = b["prompts"]
+        prompts[i, -p_len:] = b["prompts"]
+        prompt_mask[i, -p_len:] = True
         rewards[i, : b["rewards"].size(0)] = b["rewards"]
         for g in range(min(G, len(b["responses"]))):
             r_len = b["responses"][g].size(0)
@@ -217,6 +220,7 @@ def grpo_collate_fn(batch: List[Dict[str, Tensor]]) -> Dict[str, Tensor]:
 
     return {
         "prompts": prompts,
+        "prompt_mask": prompt_mask,
         "responses": responses,
         "masks": masks,
         "rewards": rewards,

astrai/trainer/rollout.py

@@ -35,6 +35,7 @@ class RawRollout:
 
     Fields:
         prompts: Tokenized prompts, shape ``[B, P_len]``.
+        prompt_mask: Boolean mask for real prompt tokens, shape ``[B, P_len]``.
         responses: Generated response token IDs, shape ``[B, G, R_max]``.
         response_mask: Boolean mask for real (non-pad) response tokens,
             shape ``[B, G, R_max]``.
@@ -47,6 +48,7 @@ class RawRollout:
     """
 
     prompts: Tensor
+    prompt_mask: Tensor
     responses: Tensor
     response_mask: Tensor
     logprobs_old: Tensor
@@ -143,6 +145,15 @@ 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)
+
+    def _generate_eval(self, batch: Dict) -> RawRollout:
         prompt_texts, flat_prompt_ids = self._prepare_prompts(batch)
         B = len(prompt_texts)
         G = self.group_size
@@ -161,6 +172,15 @@ class RolloutGenerator:
             rep_window=self.rep_window,
             return_logprobs=True,
         )
+        if len(results) != B * G:
+            raise RuntimeError(
+                f"Rollout scheduler returned {len(results)} results, expected {B * G}"
+            )
+        for token_ids, logprobs in results:
+            if len(token_ids) != len(logprobs):
+                raise RuntimeError(
+                    "Rollout scheduler returned misaligned token IDs and logprobs"
+                )
 
         # Each element is (token_ids, logprobs); pad to max length.
         max_len = 0
@@ -171,10 +191,12 @@ class RolloutGenerator:
         device = self.scheduler.device
         P_len = max(len(ids) for ids in flat_prompt_ids)
         prompts_tensor = torch.zeros(B, P_len, dtype=torch.long, device=device)
+        prompt_mask = torch.zeros(B, P_len, dtype=torch.bool, device=device)
         for i, ids in enumerate(flat_prompt_ids):
-            prompts_tensor[i, : len(ids)] = torch.tensor(
+            prompts_tensor[i, -len(ids) :] = torch.tensor(
                 ids, dtype=torch.long, device=device
             )
+            prompt_mask[i, -len(ids) :] = True
 
         responses = torch.full((B, G, max_len), _PAD, dtype=torch.long, device=device)
         response_mask = torch.zeros((B, G, max_len), dtype=torch.bool, device=device)
@@ -201,6 +223,7 @@ class RolloutGenerator:
 
         return RawRollout(
             prompts=prompts_tensor,
+            prompt_mask=prompt_mask,
             responses=responses,
             response_mask=response_mask,
             logprobs_old=logprobs_old,
@@ -239,17 +262,35 @@ class RolloutGenerator:
                 f"{list(batch.keys())}"
             )
 
-        prompt_texts: List[str] = []
+        try:
-        flat_prompt_ids: List[List[int]] = []
+            prompt_texts = self.tokenizer.apply_chat_template(
-        for messages in messages_list:
+                messages_list, tokenize=False, add_generation_prompt=True
-            text = self.tokenizer.apply_chat_template(
-                messages, tokenize=False, add_generation_prompt=True
             )
-            ids = self.tokenizer.apply_chat_template(
+            if (
-                messages, tokenize=True, add_generation_prompt=True
+                not isinstance(prompt_texts, list)
-            )
+                or len(prompt_texts) != len(messages_list)
-            prompt_texts.append(text)
+                or not all(isinstance(text, str) for text in prompt_texts)
-            flat_prompt_ids.append(list(ids))
+            ):
+                raise TypeError("Tokenizer does not support batched chat templates")
+            flat_prompt_ids = self.tokenizer.encode(prompt_texts)
+            if len(flat_prompt_ids) != len(messages_list) or not all(
+                isinstance(ids, list) for ids in flat_prompt_ids
+            ):
+                raise TypeError("Tokenizer does not support batched encoding")
+        except (TypeError, IndexError, KeyError):
+            # Keep compatibility with lightweight tokenizer adapters that only
+            # implement the single-conversation template API.
+            prompt_texts = []
+            flat_prompt_ids = []
+            for messages in messages_list:
+                text = self.tokenizer.apply_chat_template(
+                    messages, tokenize=False, add_generation_prompt=True
+                )
+                ids = self.tokenizer.apply_chat_template(
+                    messages, tokenize=True, add_generation_prompt=True
+                )
+                prompt_texts.append(text)
+                flat_prompt_ids.append(list(ids))
         return prompt_texts, flat_prompt_ids
 
     @staticmethod
@@ -308,6 +349,7 @@ class RolloutRunner:
         self.rollout_interval = rollout_interval
 
         self._cache: Optional[RolloutResult] = None
+        self._cache_key = None
         self._steps_since_rollout: int = 0
 
     def step(self):
@@ -317,12 +359,40 @@ class RolloutRunner:
     def clear_cache(self):
         """Force next call to re-run rollout."""
         self._cache = None
+        self._cache_key = None
+
+    @staticmethod
+    def _batch_key(batch: Dict):
+        """Build a stable key for the prompt fields accepted by the generator."""
+
+        def freeze(value):
+            if isinstance(value, dict):
+                return tuple(sorted((key, freeze(val)) for key, val in value.items()))
+            if isinstance(value, (list, tuple)):
+                return tuple(freeze(item) for item in value)
+            return value
+
+        fields = ("messages", "instruction", "input", "output")
+        return tuple(
+            (field, freeze(batch[field])) for field in fields if field in batch
+        )
 
     def _score(self, raw: RawRollout) -> RolloutResult:
         rewards = self.reward_model.score(raw.prompt_texts, raw.response_texts)
+        if not isinstance(rewards, Tensor):
+            rewards = torch.as_tensor(rewards, dtype=torch.float32)
+        expected_shape = raw.responses.shape[:2]
+        if rewards.shape != expected_shape:
+            raise ValueError(
+                f"Reward model returned shape {tuple(rewards.shape)}, "
+                f"expected {tuple(expected_shape)}"
+            )
+        if not torch.isfinite(rewards).all():
+            raise ValueError("Reward model returned non-finite values")
         device = raw.prompts.device
         return RolloutResult(
             prompts=raw.prompts,
+            prompt_mask=raw.prompt_mask,
             responses=raw.responses,
             response_mask=raw.response_mask,
             rewards=rewards.to(device=device),
@@ -337,9 +407,15 @@ class RolloutRunner:
         Triggers a new rollout when ``_steps_since_rollout >= rollout_interval``
         or when the cache is empty.
         """
-        if self._cache is None or self._steps_since_rollout >= self.rollout_interval:
+        cache_key = self._batch_key(batch)
+        if (
+            self._cache is None
+            or cache_key != self._cache_key
+            or self._steps_since_rollout >= self.rollout_interval
+        ):
             raw = self.generator.generate(batch)
             self._cache = self._score(raw)
+            self._cache_key = cache_key
             self._steps_since_rollout = 0
             return self._cache, True
         return self._cache, False

astrai/trainer/strategy.py

@@ -158,6 +158,11 @@ class BaseStrategy(ABC):
         """
         pass
 
+    def on_optimizer_step(self):
+        """Advance online rollout state after a successful optimizer step."""
+        if self._rollout_runner is not None:
+            self._rollout_runner.step()
+
     def __call__(self, batch: Dict[str, Tensor]) -> Tensor:
         """Run offline or online forward depending on runner injection."""
         if self._rollout_runner is None:
@@ -166,8 +171,6 @@ class BaseStrategy(ABC):
         result, is_fresh = self._rollout_runner(batch)
         if is_fresh:
             self._on_rollout_refresh()
-            if self.executor and self.executor.sync_gradients:
-                self._rollout_runner.step()
 
         train_batch = self.prepare_from_rollout(result)
         return self.compute_loss(train_batch)
@@ -411,6 +414,12 @@ class GRPOStrategy(BaseStrategy):
         responses_flat = responses.view(-1, response_len)
         masks_flat = masks.view(-1, response_len)
         prompt_expanded = prompts.unsqueeze(1).repeat(1, group_size, 1).flatten(0, 1)
+        prompt_mask = batch.get("prompt_mask")
+        if prompt_mask is None:
+            prompt_mask = prompts.ne(0)
+        prompt_mask_expanded = (
+            prompt_mask.unsqueeze(1).expand(-1, group_size, -1).flatten(0, 1)
+        )
         prompt_len = prompt_expanded.size(1)
 
         full_sequences = torch.cat([prompt_expanded, responses_flat], dim=-1)
@@ -423,7 +432,9 @@ class GRPOStrategy(BaseStrategy):
         )
 
         # Build full attention mask: key-padding + causal
-        key_pad = full_sequences.bool()[:, None, None, :]
+        key_pad = torch.cat([prompt_mask_expanded, masks_flat.bool()], dim=-1)[
+            :, None, None, :
+        ]
         S = key_pad.shape[-1]
         causal = torch.tril(
             torch.ones(S, S, dtype=torch.bool, device=full_sequences.device)
@@ -485,6 +496,7 @@ class GRPOStrategy(BaseStrategy):
     def prepare_from_rollout(self, result: RolloutResult) -> Dict[str, Tensor]:
         return {
             "prompts": result.prompts,
+            "prompt_mask": result.prompt_mask,
             "responses": result.responses,
             "masks": result.response_mask,
             "rewards": result.rewards,

astrai/trainer/trainer.py

@@ -83,6 +83,7 @@ class Trainer:
                         if executor.sync_gradients:
                             self._call_callbacks("on_optimizer_step", context)
                             context.optimizer.step()
+                            context.strategy.on_optimizer_step()
                             context.optimizer.zero_grad()
 
                             if context.scheduler:

tests/data/test_dataset.py

@@ -937,8 +937,9 @@ def test_grpo_collate_variable_lengths():
     assert result["masks"].shape == (2, 2, 4)
     assert result["rewards"].shape == (2, 2)
 
-    # Check padding: item 1 prompt is length 2, padded to 3
+    # Prompts are left-padded so each response follows its real prompt tokens.
-    assert result["prompts"][1, 2] == 0
+    assert torch.equal(result["prompts"][1], torch.tensor([0, 10, 11]))
+    assert torch.equal(result["prompt_mask"][1], torch.tensor([False, True, True]))
 
     # Check response content: item 0, response 0 is [4,5] padded to 4
     assert result["responses"][0, 0, 0] == 4

tests/trainer/test_online_strategy.py

@@ -63,6 +63,7 @@ def _make_frozen(model, device):
 def _make_rollout_result(B=2, G=4, P=6, R=8, device="cpu"):
     return RolloutResult(
         prompts=torch.randint(3, 200, (B, P), device=device),
+        prompt_mask=torch.ones(B, P, dtype=torch.bool, device=device),
         responses=torch.randint(3, 200, (B, G, R), device=device),
         response_mask=torch.ones(B, G, R, dtype=torch.bool, device=device),
         rewards=torch.randn(B, G, device=device),
@@ -177,6 +178,7 @@ def test_grpo_prepare_from_rollout_mapping(device):
     r = _make_rollout_result(device=device)
     batch = strat.prepare_from_rollout(r)
     assert batch["prompts"] is r.prompts
+    assert batch["prompt_mask"] is r.prompt_mask
     assert batch["responses"] is r.responses
     assert batch["masks"] is r.response_mask
     assert batch["rewards"] is r.rewards
@@ -255,9 +257,11 @@ def test_grpo_no_resync_when_same_cached_result(device):
     runner = _RecordingRunner(_make_rollout_result(device=device))
     strat.set_rollout_runner(runner)
     strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
+    strat.on_optimizer_step()
     strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
+    strat.on_optimizer_step()
     assert runner.calls == 2
-    assert runner.step_calls == 1
+    assert runner.step_calls == 2
 
 
 def test_grpo_resync_when_new_rollout_result(device):
@@ -265,8 +269,10 @@ def test_grpo_resync_when_new_rollout_result(device):
     runner = _RecordingRunner(_make_rollout_result(device=device))
     strat.set_rollout_runner(runner)
     strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
+    strat.on_optimizer_step()
     runner.swap_result(_make_rollout_result(device=device))
     strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
+    strat.on_optimizer_step()
     assert runner.calls == 2
     assert runner.step_calls == 2
 
@@ -281,8 +287,10 @@ def test_dpo_no_sync_hook_when_new_rollout_result(device):
     runner = _RecordingRunner(_make_rollout_result(device=device))
     strat.set_rollout_runner(runner)
     strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
+    strat.on_optimizer_step()
     runner.swap_result(_make_rollout_result(device=device))
     strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
+    strat.on_optimizer_step()
     assert runner.step_calls == 2
 
 
@@ -301,6 +309,7 @@ def test_step_called_when_sync_gradients_true(device):
     runner = _RecordingRunner(_make_rollout_result(device=device))
     strat.set_rollout_runner(runner)
     strat({"input_ids": torch.randint(3, 200, (2, 4), device=device)})
+    strat.on_optimizer_step()
     assert runner.step_calls == 1
 
 

tests/trainer/test_rollout.py

@@ -71,6 +71,18 @@ class ConstantRewardModel(BaseRewardModel):
         return torch.full((B, G), float(self.value))
 
 
+class BadShapeRewardModel(BaseRewardModel):
+    def score(self, prompts, responses):
+        return torch.zeros(len(prompts))
+
+
+class NonFiniteRewardModel(BaseRewardModel):
+    def score(self, prompts, responses):
+        B = len(prompts)
+        G = len(responses[0]) if B else 0
+        return torch.full((B, G), float("nan"))
+
+
 def _make_config(vocab_size=200, max_position_embeddings=128):
     return AutoRegressiveLMConfig(
         vocab_size=vocab_size,
@@ -111,6 +123,7 @@ def _make_instruction_batch(n=2):
 def test_raw_rollout_fields():
     r = RawRollout(
         prompts=torch.zeros(2, 4, dtype=torch.long),
+        prompt_mask=torch.ones(2, 4, dtype=torch.bool),
         responses=torch.zeros(2, 3, 5, dtype=torch.long),
         response_mask=torch.ones(2, 3, 5, dtype=torch.bool),
         logprobs_old=torch.zeros(2, 3, 5),
@@ -124,6 +137,7 @@ def test_raw_rollout_fields():
 def test_rollout_result_inherits_raw_rollout_fields():
     r = RolloutResult(
         prompts=torch.zeros(2, 4, dtype=torch.long),
+        prompt_mask=torch.ones(2, 4, dtype=torch.bool),
         responses=torch.zeros(2, 3, 5, dtype=torch.long),
         response_mask=torch.ones(2, 3, 5, dtype=torch.bool),
         logprobs_old=torch.zeros(2, 3, 5),
@@ -132,6 +146,7 @@ def test_rollout_result_inherits_raw_rollout_fields():
     assert r.rewards.shape == (2, 3)
     assert r.prompts.shape == (2, 4)
     assert r.responses.shape == (2, 3, 5)
+    assert r.prompt_mask.shape == (2, 4)
 
 
 def test_base_reward_model_is_abstract():
@@ -179,11 +194,28 @@ def test_rollout_generator_shapes(device):
     assert r.responses.shape == (2, 3, 5)
     assert r.response_mask.shape == (2, 3, 5)
     assert r.logprobs_old.shape == (2, 3, 5)
+    assert r.prompt_mask.shape == r.prompts.shape
     assert len(r.prompt_texts) == 2
     assert len(r.response_texts) == 2
     assert len(r.response_texts[0]) == 3
 
 
+def test_rollout_generator_uses_eval_and_restores_mode(device):
+    gen, model = _make_generator(device, group_size=1, max_tokens=2)
+    model.train()
+    seen_training = []
+    original = gen.scheduler.run_batch
+
+    def recording_run_batch(*args, **kwargs):
+        seen_training.append(model.training)
+        return original(*args, **kwargs)
+
+    gen.scheduler.run_batch = recording_run_batch
+    gen.generate(_make_instruction_batch(n=1))
+    assert seen_training == [False]
+    assert model.training is True
+
+
 def test_rollout_generator_mask_matches_responses(device):
     """Positions beyond a response's length are pad (mask False)."""
     gen, _ = _make_generator(device, group_size=2, max_tokens=6)
@@ -291,6 +323,24 @@ def test_rollout_runner_cache_returns_stale_flag(device):
     assert fresh2 is False
 
 
+def test_rollout_runner_refreshes_for_different_batch(device):
+    runner, _ = _make_runner(device, rollout_interval=100)
+    r1, fresh1 = runner(_make_instruction_batch(n=1))
+    batch2 = {"instruction": ["Different prompt"], "input": [""]}
+    r2, fresh2 = runner(batch2)
+    assert fresh1 is True
+    assert fresh2 is True
+    assert r2 is not r1
+
+
+@pytest.mark.parametrize("reward_model", [BadShapeRewardModel, NonFiniteRewardModel])
+def test_rollout_runner_rejects_invalid_rewards(device, reward_model):
+    generator, _ = _make_generator(device, group_size=2, max_tokens=2)
+    runner = RolloutRunner(generator, reward_model(), rollout_interval=1)
+    with pytest.raises(ValueError):
+        runner(_make_instruction_batch(n=1))
+
+
 def test_rollout_runner_step_triggers_new_rollout(device):
     runner, _ = _make_runner(device, rollout_interval=2)
     batch = _make_instruction_batch()