diff --git a/configs/examples/value_config.json b/configs/examples/value_config.json
index b4a23d5..5eac26c 100644
--- a/configs/examples/value_config.json
+++ b/configs/examples/value_config.json
@@ -2,11 +2,14 @@
     "dataset_mixture": {
         "datasets": [
             {
-                "repo_id": "physical-intelligence/libero",
-                "episodes": [0,1,2,3,4,5,6,7,8,9]
+                "repo_id": "physical-intelligence/libero"
+            },
+            {
+                "grounding": "clevr"
             }
         ],
         "weights": [
+            1.0,
             1.0
         ],
         "action_freq": 30.0,
@@ -22,7 +25,8 @@
             "VALUE": "MEAN_STD"
         },
         "max_state_dim": 32,
-        "tokenizer_max_length": 52,
+        "prompt_max_length": 256,
+        "response_max_length": 52,
         "reward_config": {
             "number_of_bins": 201,
             "C_neg": -1000.0,
@@ -41,10 +45,11 @@
     "gradient_accumulation_steps": 1,
     "dataloader_batch_size": 2,
     "prefetch_factor": 2,
-    "steps": 100,
-    "log_freq": 1,
+    "steps": 10000,
+    "log_freq": 100,
+    "val_freq": 500,
     "save_checkpoint": true,
-    "save_freq": 100,
+    "save_freq": 1000,
     "use_policy_training_preset": false,
     "trace_nans": true,
     "optimizer": {
diff --git a/src/opentau/policies/value/configuration_value.py b/src/opentau/policies/value/configuration_value.py
index da042fb..5e43b19 100644
--- a/src/opentau/policies/value/configuration_value.py
+++ b/src/opentau/policies/value/configuration_value.py
@@ -77,7 +77,8 @@ class ValueConfig(PreTrainedConfig):
     empty_cameras: int = 0
 
     # Tokenizer
-    tokenizer_max_length: int = 48
+    prompt_max_length: int = 48
+    response_max_length: int = 52
 
     # Reward config
     reward_config: RewardConfig = field(default_factory=RewardConfig)
diff --git a/src/opentau/policies/value/modeling_value.py b/src/opentau/policies/value/modeling_value.py
index ceba707..83d55f9 100644
--- a/src/opentau/policies/value/modeling_value.py
+++ b/src/opentau/policies/value/modeling_value.py
@@ -21,6 +21,10 @@
 Uses SIGLIP for vision encoding and Gemma 3 270M for language processing.
 """
 
+import logging
+import math
+
+import numpy as np
 import torch
 import torch.nn.functional as F  # noqa: N812
 from einops import rearrange
@@ -228,9 +232,8 @@ def predict_value(self, batch: dict[str, Tensor]) -> Tensor:
 
         images, img_masks = self.prepare_images(batch)
         lang_tokens, lang_masks = self.prepare_language(batch)
-        state = batch.get("state")
 
-        logits = self.model.forward(images, img_masks, lang_tokens, lang_masks, state)
+        logits = self.model.get_value(images, img_masks, lang_tokens, lang_masks)
         return self.calculate_value(logits)
 
     def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict[str, Tensor] | None]:
@@ -246,22 +249,49 @@ def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict[str, Tensor] |
 
         images, img_masks = self.prepare_images(batch)
         lang_tokens, lang_masks = self.prepare_language(batch)
-        state = batch.get("state")
+        response_tokens, response_masks = self.prepare_response(batch)
 
-        logits = self.model.forward(images, img_masks, lang_tokens, lang_masks, state)
-        values = self.calculate_value(logits)
+        ce_logits, response_logits = self.model.forward(
+            images, img_masks, lang_tokens, lang_masks, response_tokens, response_masks
+        )
+        values = self.calculate_value(ce_logits)
         # Compute Cross-Entropy loss
-        logits = logits.to(dtype=torch.float32)  # upcast to float32 for loss calculation
+        ce_logits = ce_logits.to(dtype=torch.float32)  # upcast to float32 for loss calculation
         batch["return_bin_idx"] = batch["return_bin_idx"].to(dtype=torch.long)
-        loss = F.cross_entropy(logits, batch["return_bin_idx"])
+        ce_loss = F.cross_entropy(ce_logits, batch["return_bin_idx"], reduction="none")
+
+        action_is_pad = batch.get("action_is_pad")
+        # Mask CE loss if all action_is_pad are true. This is used for VQA dataset where we don't have actions tokens.
+        ce_loss = ce_loss * (~action_is_pad.all(dim=1)).float()
+
+        ce_loss = ce_loss.mean()
 
         l1_loss = F.l1_loss(values, batch["return_continuous"])
 
-        accuracy = (logits.argmax(dim=-1) == batch["return_bin_idx"]).float().mean()
+        accuracy = (ce_logits.argmax(dim=-1) == batch["return_bin_idx"]).float().mean()
+
+        batch_size, seq_len = response_logits.shape[0], response_logits.shape[1]
+        response_slice = slice(1, None)
+        response_logits = response_logits.to(dtype=torch.float32)  # upcast to float32 for loss calculation
+        response_logits = rearrange(response_logits, "b s d -> (b s) d")
+        response_labels = rearrange(response_tokens[:, response_slice], "b s -> (b s)")
+        response_ce_loss = F.cross_entropy(response_logits, response_labels, reduction="none")
+
+        response_ce_loss = rearrange(response_ce_loss, "(b s) -> b s", b=batch_size, s=seq_len)
+
+        # remove pad tokens
+        response_is_pad = ~response_masks  # convert into format where value for pad is True
+        # Mask response loss if response is padded
+        response_ce_loss = response_ce_loss * ~response_is_pad[:, response_slice]
+        # Mask response loss if all action_is_pad are true. This is used for Robotic dataset where we have at least one actions tokens.
+        response_ce_loss = response_ce_loss * rearrange((action_is_pad.all(dim=1)).float(), "b -> b 1")
+
+        # compute mean
+        response_ce_loss = response_ce_loss.mean()
 
         return {
-            "MSE": torch.zeros_like(loss, requires_grad=False),
-            "CE": loss,
+            "MSE": torch.zeros_like(ce_loss, requires_grad=False),
+            "CE": ce_loss + response_ce_loss,
             "L1": l1_loss,
             "Accuracy": accuracy,
         }
@@ -321,6 +351,35 @@ def prepare_images(self, batch):
 
         return images, img_masks
 
+    def prepare_discrete_state(self, batch: dict[str, Tensor]) -> list[str]:
+        """Discretizes the state into bins and converts it to a string representation.
+
+        Each dimension of the state vector is discretized into 256 bins.
+        The values of each dimension of the state are expected to be in the range [-1, 1].
+        The discretization bins are linearly spaced between -1 and 1.
+        The index of the bin for each dimension is then concatenated into a space-separated string.
+
+        Args:
+            batch: Batch of data containing the "state" tensor.
+
+        Returns:
+            A list of strings, where each string is a space-separated list of discretized state values.
+
+        Raises:
+            ValueError: If the state values are not normalized between -1 and 1.
+        """
+        state = batch["state"]
+        state_np = state.to(device="cpu", dtype=torch.float32).numpy()
+        if np.any(state_np < -1.0) or np.any(state_np > 1.0):
+            logging.warning(
+                f"State values are not normalized between -1 and 1. Min: {state_np.min()}, Max: {state_np.max()}"
+            )
+        state_np = np.clip(state_np, -1.0, 1.0)
+        discretized_states = np.digitize(state_np, bins=np.linspace(-1, 1, 256 + 1)[:-1]) - 1
+        return [
+            " ".join(map(str, row)) for row in discretized_states
+        ]  # TODO: return a tensor instead of a list of strings?
+
     def prepare_language(self, batch) -> tuple[Tensor, Tensor]:
         """Tokenizes the text input for the model.
 
@@ -333,21 +392,54 @@ def prepare_language(self, batch) -> tuple[Tensor, Tensor]:
         device = batch.get("state", list(batch.values())[0]).device
         tasks = batch["prompt"]
 
-        # PaliGemma prompt has to end with a new line
-        tasks = [task if task.endswith("\n") else f"{task}\n" for task in tasks]
+        state = self.prepare_discrete_state(batch)
+        # using <eos> to separate each modality
+        prompt = [f"Task: {task}<eos>State: {state}<eos>" for task, state in zip(tasks, state, strict=False)]
 
         tokenized_prompt = self.language_tokenizer.__call__(
-            tasks,
+            prompt,
             padding="max_length",
             padding_side="right",
-            max_length=self.config.tokenizer_max_length,
+            max_length=self.config.prompt_max_length,
             return_tensors="pt",
+            truncation=True,
         )
         lang_tokens = tokenized_prompt["input_ids"].to(device=device)
         lang_masks = tokenized_prompt["attention_mask"].to(device=device, dtype=torch.bool)
 
         return lang_tokens, lang_masks
 
+    def prepare_response(self, batch: dict[str, Tensor]) -> tuple[Tensor, Tensor]:
+        """Tokenize the response input.
+
+        Args:
+            batch: Batch of data containing the key "response".
+
+        Returns:
+            A tuple containing:
+                - response_tokens: Tensor of response language tokens.
+                - response_masks: Tensor of response language attention masks.
+        """
+
+        device = batch["state"].device
+        responses = batch["response"]
+
+        # if '' is found in response then response is not for loss calculation (used for robotic dataset with no subtask), so add pad token to the response.
+        response_prompt = [f"{response}" for response in responses]
+
+        tokenized_response = self.language_tokenizer.__call__(
+            response_prompt,
+            padding="max_length",
+            padding_side="right",
+            max_length=self.config.response_max_length,
+            return_tensors="pt",
+            truncation=True,
+        )
+        response_tokens = tokenized_response["input_ids"].to(device=device)
+        response_masks = tokenized_response["attention_mask"].to(device=device, dtype=torch.bool)
+
+        return response_tokens, response_masks
+
 
 class ValueModel(nn.Module):
     """
@@ -376,8 +468,6 @@ class ValueModel(nn.Module):
     └──────────────────────────────┘
     """
 
-    CLASSIFICATION_TOKEN_ID = 6  # unused token id in Gemma 3 270M that we repurpose for classification
-
     def __init__(self, config):
         """Initializes the ValueModel.
 
@@ -388,7 +478,8 @@ def __init__(self, config):
         self.config = config
 
         siglip_gemma_value_config = SiglipGemmaValueConfig(
-            num_value_bins=self.config.reward_config.number_of_bins
+            num_value_bins=self.config.reward_config.number_of_bins,
+            response_max_length=self.config.response_max_length,
         )
         self.siglip_gemma_value = SiglipGemmaValueModel(siglip_gemma_value_config)
 
@@ -399,7 +490,13 @@ def __init__(self, config):
         self.c_neg = config.reward_config.C_neg
 
     def embed_sequence(
-        self, images, img_masks, lang_tokens, lang_masks, state
+        self,
+        images,
+        img_masks,
+        lang_tokens,
+        lang_masks,
+        response_tokens: torch.Tensor | None = None,
+        response_masks: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """Embeds sequence of images and language tokens.
 
@@ -451,25 +548,19 @@ def embed_sequence(
         num_lang_embs = lang_emb.shape[1]
         att_masks += [0] * num_lang_embs
 
-        # embed state
-        state_emb = self.state_proj(state)
-        state_emb = state_emb.to(dtype=torch.bfloat16)
-        embs.append(state_emb[:, None, :])
+        if response_tokens is not None:
+            response_emb = self.siglip_gemma_value.embed_language_tokens(response_tokens)
 
-        state_mask = torch.ones(state_emb.shape[0], 1, dtype=torch.bool, device=state_emb.device)
-        pad_masks.append(state_mask)
+            # Normalize response language embeddings
+            response_emb_dim = response_emb.shape[-1]
+            response_emb = response_emb * math.sqrt(response_emb_dim)
 
-        # full attention between state and image and language inputs
-        att_masks += [0]
+            embs.append(response_emb)
+            pad_masks.append(response_masks)
 
-        # add classification token
-        cls_token = torch.full(
-            (bsize, 1), self.CLASSIFICATION_TOKEN_ID, device=state_emb.device, dtype=torch.long
-        )
-        cls_token_emb = self.siglip_gemma_value.gemma.embed_tokens(cls_token)
-        embs.append(cls_token_emb)
-        pad_masks.append(torch.ones(bsize, 1, dtype=torch.bool, device=state_emb.device))
-        att_masks += [0]
+            # full attention between image, language and response inputs
+            num_response_embs = response_emb.shape[1]
+            att_masks += [1] * num_response_embs
 
         embs = torch.cat(embs, dim=1)
         pad_masks = torch.cat(pad_masks, dim=1)
@@ -484,7 +575,42 @@ def forward(
         img_masks: list[torch.Tensor],
         lang_tokens: torch.Tensor,
         lang_masks: torch.Tensor,
-        state: torch.Tensor | None = None,
+        response_tokens: torch.Tensor | None = None,
+        response_masks: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """Predict value estimates given observations.
+
+        Args:
+            images: List of image tensors
+            img_masks: List of image masks
+            lang_tokens: Language token IDs
+            lang_masks: Language attention masks
+            state: Optional state tensor
+
+        Returns:
+            Tensor of shape [batch_size, 1] containing value estimates
+        """
+        embs, pad_masks, att_masks = self.embed_sequence(
+            images, img_masks, lang_tokens, lang_masks, response_tokens, response_masks
+        )
+
+        att_2d_masks = make_att_2d_masks(pad_masks, att_masks)
+        position_ids = torch.cumsum(pad_masks, dim=1) - 1
+
+        ce_logits, response_logits = self.siglip_gemma_value.forward(
+            inputs_embeds=embs,
+            attention_mask=att_2d_masks,
+            position_ids=position_ids,
+        )
+
+        return ce_logits, response_logits
+
+    def get_value(
+        self,
+        images: list[torch.Tensor],
+        img_masks: list[torch.Tensor],
+        lang_tokens: torch.Tensor,
+        lang_masks: torch.Tensor,
     ) -> torch.Tensor:
         """Predict value estimates given observations.
 
@@ -498,15 +624,15 @@ def forward(
         Returns:
             Tensor of shape [batch_size, 1] containing value estimates
         """
-        embs, pad_masks, att_masks = self.embed_sequence(images, img_masks, lang_tokens, lang_masks, state)
+        embs, pad_masks, att_masks = self.embed_sequence(images, img_masks, lang_tokens, lang_masks)
 
         att_2d_masks = make_att_2d_masks(pad_masks, att_masks)
         position_ids = torch.cumsum(pad_masks, dim=1) - 1
 
-        logits = self.siglip_gemma_value.forward(
+        value_logits = self.siglip_gemma_value.get_value(
             inputs_embeds=embs,
             attention_mask=att_2d_masks,
             position_ids=position_ids,
         )
 
-        return logits
+        return value_logits
diff --git a/src/opentau/policies/value/siglip_gemma.py b/src/opentau/policies/value/siglip_gemma.py
index dafbe5f..f50109e 100644
--- a/src/opentau/policies/value/siglip_gemma.py
+++ b/src/opentau/policies/value/siglip_gemma.py
@@ -50,9 +50,11 @@ def __init__(
         siglip_config: dict | None = None,
         gemma_config: dict | None = None,
         num_value_bins: int = 201,
+        response_max_length: int = 52,
         **kwargs,
     ):
         self.num_value_bins = num_value_bins
+        self.response_max_length = response_max_length
 
         if siglip_config is None:
             # Default SIGLIP config similar to PaliGemma vision config
@@ -152,6 +154,8 @@ def __init__(self, config: SiglipGemmaValueConfig):
 
         # Value head: projects final hidden state to discretized value bins
         self.value_head = nn.Linear(640, config.num_value_bins)
+        # Response head: projects response hidden states to logits for response language
+        self.response_head = nn.Linear(640, self.gemma.config.vocab_size, bias=False)
 
     def embed_image(self, image: torch.Tensor) -> torch.Tensor:
         """Embeds images using the SIGLIP vision encoder.
@@ -213,9 +217,57 @@ def forward(
 
         # Extract the last token's hidden state for value prediction
         # Use the last token (which should be the last language token)
-        final_hidden = hidden_states[:, -1]
+
+        # extract token just before response <bos> token
+        classification_hidden = hidden_states[:, -self.config.response_max_length - 1, :]
+        # extract tokens from response <bos> token to just before last token
+        response_hidden = hidden_states[:, -self.config.response_max_length : -1, :]
+
+        # Project to logits for discretized values
+        ce_logits = self.value_head(classification_hidden)
+        # project response hidden states to logits for response language
+        response_logits = self.response_head(response_hidden)
+
+        return ce_logits, response_logits
+
+    def get_value(
+        self,
+        inputs_embeds: torch.FloatTensor,
+        attention_mask: torch.Tensor,
+        position_ids: torch.LongTensor,
+    ) -> torch.Tensor:
+        """Forward pass that processes vision and language inputs and outputs a value.
+
+        Args:
+            inputs_embeds: Tensor of shape [batch_size, sequence_length, embedding_dim]
+                containing the combined embeddings of images and text.
+            attention_mask: Attention mask for the sequence.
+            position_ids: Position IDs for RoPE.
+
+        Returns:
+            torch.Tensor: Logits for discretized values of shape [batch_size, num_value_bins].
+        """
+
+        attention_mask = rearrange(attention_mask, "b n1 n2 -> b 1 n1 n2")  # support multihead attention
+        # HACK: use full attention for sliding attention as well since our context length is almost the same size as the sliding window
+        mask_mapping = {
+            "full_attention": attention_mask,
+            "sliding_attention": attention_mask,
+        }
+        outputs = self.gemma(
+            inputs_embeds=inputs_embeds,
+            position_ids=position_ids,
+            attention_mask=mask_mapping,
+        )
+        hidden_states = outputs.last_hidden_state
+
+        # Extract the last token's hidden state for value prediction
+        # Use the last token (which should be the last language token)
+
+        # extract last token while inference
+        value_hidden = hidden_states[:, -1, :]
 
         # Project to logits for discretized values
-        logits = self.value_head(final_hidden)
+        value_logits = self.value_head(value_hidden)
 
-        return logits
+        return value_logits
diff --git a/src/opentau/scripts/inference.py b/src/opentau/scripts/inference.py
index a8b0d1f..5128934 100644
--- a/src/opentau/scripts/inference.py
+++ b/src/opentau/scripts/inference.py
@@ -58,7 +58,7 @@ def inference_main(cfg: TrainPipelineConfig):
 
     with torch.inference_mode():
         for _ in range(1000):
-            action = policy.select_action(observation)
+            action = policy.predict_value(observation)
             action = action.to("cpu", torch.float32).numpy()
             print(f"Output shape: {action.shape}")
 
diff --git a/uv.lock b/uv.lock
index 979a40a..82ef61b 100644
--- a/uv.lock
+++ b/uv.lock
@@ -21,11 +21,11 @@ conflicts = [[
 
 [[package]]
 name = "absl-py"
-version = "2.3.1"
+version = "2.4.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811, upload-time = "2025-07-03T09:31:42.253Z" },
+    { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
 ]
 
 [[package]]
@@ -423,23 +423,23 @@ wheels = [
 
 [[package]]
 name = "coverage"
-version = "7.13.1"
+version = "7.13.2"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/23/f9/e92df5e07f3fc8d4c7f9a0f146ef75446bf870351cd37b788cf5897f8079/coverage-7.13.1.tar.gz", hash = "sha256:b7593fe7eb5feaa3fbb461ac79aac9f9fc0387a5ca8080b0c6fe2ca27b091afd", size = 825862, upload-time = "2025-12-28T15:42:56.969Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/ad/49/349848445b0e53660e258acbcc9b0d014895b6739237920886672240f84b/coverage-7.13.2.tar.gz", hash = "sha256:044c6951ec37146b72a50cc81ef02217d27d4c3640efd2640311393cbbf143d3", size = 826523, upload-time = "2026-01-25T13:00:04.889Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/2d/9a/3742e58fd04b233df95c012ee9f3dfe04708a5e1d32613bd2d47d4e1be0d/coverage-7.13.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e1fa280b3ad78eea5be86f94f461c04943d942697e0dac889fa18fff8f5f9147", size = 218633, upload-time = "2025-12-28T15:40:10.165Z" },
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