diff --git a/pyproject.toml b/pyproject.toml
index 4ba84770f..b9c9fbfb9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,7 @@ license = {text = "Apache 2"}
 dependencies = [
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 dynamic = ["version"]
diff --git a/uv.lock b/uv.lock
index cfa9cf01c..df4670601 100644
--- a/uv.lock
+++ b/uv.lock
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     { url = "https://files.pythonhosted.org/packages/f5/4b/a06e0ec3d155924f77835ed2d167ebd3b211a7b0853da1cf8d8414d784ef/yarl-1.18.3-py3-none-any.whl", hash = "sha256:b57f4f58099328dfb26c6a771d09fb20dbbae81d20cfb66141251ea063bd101b", size = 45109, upload-time = "2024-12-01T20:35:20.834Z" },
 ]
-
-[[package]]
-name = "zipp"
-version = "3.21.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/3f/50/bad581df71744867e9468ebd0bcd6505de3b275e06f202c2cb016e3ff56f/zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4", size = 24545, upload-time = "2024-11-10T15:05:20.202Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/b7/1a/7e4798e9339adc931158c9d69ecc34f5e6791489d469f5e50ec15e35f458/zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931", size = 9630, upload-time = "2024-11-10T15:05:19.275Z" },
-]
diff --git a/vllm_spyre/v1/worker/spyre_model_runner.py b/vllm_spyre/v1/worker/spyre_model_runner.py
index cc0b35132..f63c1824d 100644
--- a/vllm_spyre/v1/worker/spyre_model_runner.py
+++ b/vllm_spyre/v1/worker/spyre_model_runner.py
@@ -244,34 +244,36 @@ def update_states(self, scheduler_output: SchedulerOutput):
         # `num_tokens`. For continuous batching it cleans
         # finished requests from the batch
         #
-        # NOTE: req_state.output_token_ids is being mutated.
-
-        for req_data in scheduler_output.scheduled_cached_reqs:
-            req_id = req_data.req_id
-            req_state = self.requests[req_id]
+        # NOTE: req_state.output_token_ids will be mutated when
+        # PP will be enabled in the future
+        req_data = scheduler_output.scheduled_cached_reqs
+        for i, req_id in enumerate(req_data.req_ids):
+            req_state: CachedRequestState = self.requests[req_id]
 
             # Update the cached states.
-            num_computed_tokens = req_data.num_computed_tokens
+            num_computed_tokens = req_data.num_computed_tokens[i]
+            # The scheduler will send the sampled tokens back
+            # when PP will be enabled in the future
+            new_token_ids = req_data.new_token_ids[i] if len(
+                req_data.new_token_ids) > 0 else []
             # Add the sampled token(s) from the previous step (if any).
             # This doesn't include "unverified" tokens like spec decode tokens.
-            num_new_tokens = (num_computed_tokens +
-                              len(req_data.new_token_ids) -
+            num_new_tokens = (num_computed_tokens + len(new_token_ids) -
                               req_state.num_tokens)
             if num_new_tokens == 1:
                 # Avoid slicing list in most common case.
-                req_state.output_token_ids.append(req_data.new_token_ids[-1])
+                req_state.output_token_ids.append(new_token_ids[-1])
             elif num_new_tokens > 0:
                 req_state.output_token_ids.extend(
-                    req_data.new_token_ids[-num_new_tokens:])
+                    new_token_ids[-num_new_tokens:])
 
             req_index = self.input_batch.get_req_index(req_id)
             # Add new_token_ids to token_ids_cpu.
             # TODO: Update for spec decoding in the future
             start_token_index = num_computed_tokens
-            end_token_index = num_computed_tokens + len(req_data.new_token_ids)
+            end_token_index = num_computed_tokens + len(new_token_ids)
             self.input_batch.token_ids_cpu[
-                req_index,
-                start_token_index:end_token_index] = req_data.new_token_ids
+                req_index, start_token_index:end_token_index] = new_token_ids
             # Remove the entry for prompt_logprobs for this request,
             # if it exists
             self.input_batch.num_prompt_logprobs.pop(req_id, None)
@@ -351,8 +353,7 @@ def _get_prompt_logprobs_dict(
     def _prepare_prompt(self, _: list[NewRequestData]) -> ModelForwardInputs:
         raise NotImplementedError
 
-    def _prepare_decode(self,
-                        _: list[CachedRequestData]) -> ModelForwardInputs:
+    def _prepare_decode(self, _: CachedRequestData) -> ModelForwardInputs:
         raise NotImplementedError
 
     def prepare_model_input(
@@ -365,7 +366,7 @@ def prepare_model_input(
         # Prepare input tensors.
         if is_prompt:
             # Assert no running requests
-            assert len(scheduler_output.scheduled_cached_reqs) == 0
+            assert len(scheduler_output.scheduled_cached_reqs.req_ids) == 0
 
             return self._prepare_prompt(scheduler_output.scheduled_new_reqs)
         else:
@@ -416,6 +417,16 @@ def execute_model(
         # Get mapping between requests ids to the index within the batch
         req_id_to_index = self.get_req_id_to_index(is_prefill)
 
+        # Add the sampled token(s) to the request cache
+        req_ids = (scheduler_output.scheduled_new_reqs
+                   if is_prefill else self.input_batch.sorted_requests_ids)
+        sampled_ids = output.sampled_token_ids.tolist()
+        for i, req in enumerate(req_ids):
+            req_state = self.requests[req.req_id] \
+                if not isinstance(
+                req, str) else self.requests[req]
+            req_state.output_token_ids.extend(sampled_ids[i])
+
         extra_kwargs: dict[str, Any] = {}
         if "pooler_output" in ModelRunnerOutput.__dataclass_fields__:
             extra_kwargs["pooler_output"] = None
@@ -529,19 +540,22 @@ def _prepare_prompt(
 
     def _prepare_decode(
         self,
-        cached_requests: list[CachedRequestData],
+        cached_request_data: CachedRequestData,
     ) -> ModelForwardInputs:
-        assert len(cached_requests) > 0
+        assert len(cached_request_data.req_ids) > 0
         input_tokens: list[list[int]] = [
             [0] for _ in range(self._position_ids.shape[0])
         ]
 
-        for cached_request in cached_requests:
+        for req_id in cached_request_data.req_ids:
             # TODO: Will this always just be one token ID if there's no spec
             # or jump decoding?
-            generation_token = cached_request.new_token_ids[-1]
-            input_tokens[self.input_batch.req_id_to_index[
-                cached_request.req_id]] = [generation_token]
+            req_state: CachedRequestState = self.requests[req_id]
+            output_token_ids = req_state.output_token_ids
+            generation_token = output_token_ids[-1]
+            input_tokens[self.input_batch.req_id_to_index[req_id]] = [
+                generation_token
+            ]
 
         # update position ids and attention mask
         self._update_position_ids()
@@ -826,48 +840,51 @@ def _prepare_prompt(
 
     def _prepare_decode(
         self,
-        cached_requests: list[CachedRequestData],
+        cached_request_data: CachedRequestData,
     ) -> ModelForwardInputs:
-        assert len(cached_requests) > 0
+        assert len(cached_request_data.req_ids) > 0
 
         input_tokens = []
         input_positions = []
         block_table = []
         slot_mapping = []
         left_padded_prompt_mask = []
-        self.model.indices = torch.ones(len(cached_requests),
+        self.model.indices = torch.ones(len(cached_request_data.req_ids),
                                         dtype=torch.bool,
                                         device="cpu")
 
-        assert len(self.input_batch.req_id_to_index) == len(cached_requests)
+        assert len(self.input_batch.req_id_to_index) == len(
+            cached_request_data.req_ids)
         # TODO(wallas): I think we can do better here, without sorting or
         # creating an intermediary dictionary
-        cached_reqs_map = {c.req_id: c for c in cached_requests}
+        cached_reqs_map = {
+            req_id: i
+            for i, req_id in enumerate(cached_request_data.req_ids)
+        }
         req_ids = self.input_batch.sorted_requests_ids
 
         for req_id in req_ids:
             # TODO: Will this always just be one token ID if there's no spec
             # or jump decoding?
-            cached_request = cached_reqs_map[req_id]
 
+            req_state: CachedRequestState = self.requests[req_id]
             # adding new blocks if needed
             if self.tkv // self.block_size + 1 > len(
-                    self.req_ids2blocks[cached_request.req_id]):
-                self.req_ids2blocks[cached_request.req_id].append(
-                    self.free_blocks.popleft())
-            block_table.append(self.req_ids2blocks[cached_request.req_id])
+                    self.req_ids2blocks[req_id]):
+                self.req_ids2blocks[req_id].append(self.free_blocks.popleft())
+            block_table.append(self.req_ids2blocks[req_id])
             # slot_mapping for all blocks of sequence
             start_slot = block_table[-1][-1] * self.block_size
             offset = self.tkv % self.block_size
             slot = [start_slot + offset]
             slot_mapping.append(slot)
-
-            generation_token = cached_request.new_token_ids[-1]
+            output_token_ids = req_state.output_token_ids
+            generation_token = output_token_ids[-1]
             input_tokens.append([generation_token])
-            seq_len = cached_request.num_computed_tokens
+            seq_len = cached_request_data.num_computed_tokens[
+                cached_reqs_map[req_id]]
             input_positions.append([seq_len])
 
-            req_state = self.requests[cached_request.req_id]
             left_padded_prompt_mask.append(req_state.left_padding)
 
         input_tokens = torch.tensor(input_tokens,
@@ -893,7 +910,7 @@ def _prepare_decode(
                                         dtype=torch.int64)
 
         # add pads for min decode batch size of 2 (Spyre compiler constraint)
-        if len(cached_requests) == 1:
+        if len(cached_request_data.req_ids) == 1:
             padd_seq_indices = torch.zeros(1, dtype=torch.bool, device="cpu")
             self.model.indices = torch.cat(
                 (self.model.indices, padd_seq_indices), -1)
diff --git a/vllm_spyre/v1/worker/spyre_worker.py b/vllm_spyre/v1/worker/spyre_worker.py
index 617751026..2f5bd536b 100644
--- a/vllm_spyre/v1/worker/spyre_worker.py
+++ b/vllm_spyre/v1/worker/spyre_worker.py
@@ -343,7 +343,7 @@ def _warmup_spyre_dynamic_size(self, special_token_ids):
             for i, req in enumerate(dummy_requests):
                 scheduler_output = SchedulerOutput(
                     scheduled_new_reqs=[req],
-                    scheduled_cached_reqs=[],
+                    scheduled_cached_reqs=CachedRequestData.make_empty(),
                     num_scheduled_tokens={req.req_id: prompt_len},
                     total_num_scheduled_tokens=prompt_len,
                     scheduled_spec_decode_tokens={},
@@ -357,39 +357,44 @@ def _warmup_spyre_dynamic_size(self, special_token_ids):
                 logger.info("[WARMUP] Prefill %d/%d...", i + 1, batch_size)
                 self.execute_model(scheduler_output)
 
-            # one decode iteration across all sequences
-            cached_requests = [
-                CachedRequestData(
-                    req_id=req.req_id,
-                    resumed_from_preemption=False,
-                    new_token_ids=[
-                        valid_token_ids_tensor[torch.randint(
-                            0, len(valid_token_ids_tensor), (1, )).item()]
-                    ],  # placeholder token
-                    new_block_ids=req.block_ids,
-                    num_computed_tokens=prompt_len,
-                ) for req in dummy_requests
-            ]
-
-            scheduler_output = SchedulerOutput(
-                scheduled_new_reqs=[],
-                scheduled_cached_reqs=cached_requests,
-                num_scheduled_tokens={
-                    f"warmup-{i}": 1
-                    for i in range(batch_size)
-                },
-                total_num_scheduled_tokens=batch_size,
-                scheduled_spec_decode_tokens={},
-                scheduled_encoder_inputs={},
-                num_common_prefix_blocks=0,
-                finished_req_ids=set(),
-                free_encoder_input_ids=[],
-                structured_output_request_ids={},
-                grammar_bitmask=None,
-            )
-            logger.info("[WARMUP] Decode...")
-            self.execute_model(scheduler_output)
-            self._cleanup_model_runner(request=dummy_requests)
+        # one decode iteration across all sequences
+        req_ids = []
+        new_token_ids = []
+        new_block_ids = []
+        num_computed_tokens = []
+        for req in dummy_requests:
+            req_ids.append(req.req_id)
+            new_token_ids.append([
+                valid_token_ids_tensor[torch.randint(
+                    0, len(valid_token_ids_tensor), (1, )).item()]
+            ])  # placeholder token
+            new_block_ids.append([req.block_ids])
+            num_computed_tokens.append(prompt_len)
+        cached_request_data = CachedRequestData(
+            req_ids=req_ids,
+            resumed_from_preemption=False,
+            new_token_ids=new_token_ids,
+            new_block_ids=new_block_ids,
+            num_computed_tokens=num_computed_tokens,
+        )
+
+        scheduler_output = SchedulerOutput(
+            scheduled_new_reqs=[],
+            scheduled_cached_reqs=cached_request_data,
+            num_scheduled_tokens={f"warmup-{i}": 1
+                                  for i in range(batch_size)},
+            total_num_scheduled_tokens=batch_size,
+            scheduled_spec_decode_tokens={},
+            scheduled_encoder_inputs={},
+            num_common_prefix_blocks=0,
+            finished_req_ids=set(),
+            free_encoder_input_ids=[],
+            structured_output_request_ids={},
+            grammar_bitmask=None,
+        )
+        logger.info("[WARMUP] Decode...")
+        self.execute_model(scheduler_output)
+        self._cleanup_model_runner(request=dummy_requests)
 
         # warmup_mode completes the graph compilation, but we need to do
         # one additional prefill to deploy the compiled program to the device,
@@ -397,7 +402,7 @@ def _warmup_spyre_dynamic_size(self, special_token_ids):
         # after this execution
         scheduler_output = SchedulerOutput(
             scheduled_new_reqs=[add_dummy_request],
-            scheduled_cached_reqs=[],
+            scheduled_cached_reqs=CachedRequestData.make_empty(),
             num_scheduled_tokens={add_dummy_request.req_id: prompt_len},
             total_num_scheduled_tokens=prompt_len,
             scheduled_spec_decode_tokens={},
@@ -429,7 +434,7 @@ def _cleanup_model_runner(self, request) -> None:
         # Needed to clean up the data of model runner
         scheduler_output = SchedulerOutput(
             scheduled_new_reqs=[],
-            scheduled_cached_reqs=[],
+            scheduled_cached_reqs=CachedRequestData.make_empty(),
             num_scheduled_tokens={},
             # NOTE: this means no work to do
             total_num_scheduled_tokens=0,
@@ -538,23 +543,31 @@ def _warmup_spyre_fixed_size(self, prompt_len, num_decode_tokens,
         ]
 
         # Set up dummy cached_requests for decode steps
-        cached_requests = [
-            CachedRequestData(
-                req_id=req.req_id,
-                resumed_from_preemption=False,
-                new_token_ids=[
-                    valid_token_ids_tensor[torch.randint(
-                        0, len(valid_token_ids_tensor), (1, )).item()]
-                ],  # placeholder token
-                new_block_ids=req.block_ids,
-                num_computed_tokens=req.num_computed_tokens,
-            ) for req in dummy_requests
-        ]
+        req_ids = []
+        new_token_ids = []
+        new_block_ids = []
+        num_computed_tokens = []
+        for req in dummy_requests:
+            req_ids.append(req.req_id)
+            new_token_ids.append([
+                valid_token_ids_tensor[torch.randint(
+                    0, len(valid_token_ids_tensor), (1, )).item()]
+            ])  # placeholder token
+            new_block_ids.append([req.block_ids])
+            num_computed_tokens.append(req.num_computed_tokens)
+
+        cached_request_data = CachedRequestData(
+            req_ids=req_ids,
+            resumed_from_preemption=False,
+            new_token_ids=new_token_ids,
+            new_block_ids=new_block_ids,
+            num_computed_tokens=num_computed_tokens,
+        )
 
         # Set up scheduler_output for execute_model
         scheduler_output = SchedulerOutput(
             scheduled_new_reqs=dummy_requests,
-            scheduled_cached_reqs=[],
+            scheduled_cached_reqs=cached_request_data,
             num_scheduled_tokens={i: prompt_len
                                   for i in range(batch_size)},
             total_num_scheduled_tokens=sum(prompt_len
@@ -573,7 +586,8 @@ def _warmup_spyre_fixed_size(self, prompt_len, num_decode_tokens,
         # The fixed size warmup needs to happen only in here
         with _maybe_warmup_context():
             self._warmup_model_forward_pass(scheduler_output, dummy_requests,
-                                            cached_requests, num_decode_tokens)
+                                            cached_request_data,
+                                            num_decode_tokens)
         self.perf_metrics.log("warmup 1 time",
                               time.time() - warmup_start_t,
                               batch_size=batch_size,
@@ -584,7 +598,7 @@ def _warmup_spyre_fixed_size(self, prompt_len, num_decode_tokens,
         logger.info("[WARMUP] Deploying to device...")
         warmup2_start_t = time.time()
         self._warmup_model_forward_pass(scheduler_output, dummy_requests,
-                                        cached_requests, num_decode_tokens)
+                                        cached_request_data, num_decode_tokens)
 
         warmup_end_t = time.time()
         warmup_total_t = warmup_end_t - warmup_start_t
@@ -602,17 +616,17 @@ def _warmup_model_forward_pass(
         self,
         scheduler_output: SchedulerOutput,
         requests: list[NewRequestData],
-        cached_requests: list[CachedRequestData],
+        cached_request_data: CachedRequestData,
         num_decode_tokens,
     ):
         """Handle a complete forward pass"""
         scheduler_output.scheduled_new_reqs = requests
-        scheduler_output.scheduled_cached_reqs = []
+        scheduler_output.scheduled_cached_reqs = CachedRequestData.make_empty()
         self.execute_model(scheduler_output)  # Prefill
 
         # Switch to cached requests to trigger decoding steps
         scheduler_output.scheduled_new_reqs = []
-        scheduler_output.scheduled_cached_reqs = cached_requests
+        scheduler_output.scheduled_cached_reqs = cached_request_data
         for _ in range(num_decode_tokens - 1):
             self.execute_model(scheduler_output)