diff --git a/src/liger_kernel/chunked_loss/fused_linear_preference.py b/src/liger_kernel/chunked_loss/fused_linear_preference.py
index 57afabc80..3b940f315 100644
--- a/src/liger_kernel/chunked_loss/fused_linear_preference.py
+++ b/src/liger_kernel/chunked_loss/fused_linear_preference.py
@@ -29,7 +29,7 @@ def forward(
         compute_nll_loss=True,
         compiled=True,
         use_ref_model=False,
-        # TODO: ref input
+        ref_input=None,
         ref_weight=None,
         ref_bias=None,
         **loss_kwargs,
@@ -59,6 +59,7 @@ def forward(
             compute_nll_loss (bool): Whether to compute NLL loss.
             compiled (bool): Whether to use torch compile for chunk accumulation.
             use_ref_model (bool): Whether to use a reference model for the alignment loss.
+            ref_input (torch.Tensor): Reference input tensor. Shape: (batch_size, seq_len, hidden_size).
             ref_weight (torch.Tensor): Reference weight tensor. Shape: (vocab_size, hidden_size).
             ref_bias (torch.Tensor, optional): Reference bias tensor. Shape: (vocab_size,).
             loss_kwargs (dict): Other possible arguments that a loss function might need
@@ -92,6 +93,7 @@ def forward(
             compute_nll_loss=compute_nll_loss,
             full_target=target,
             use_ref_model=use_ref_model,
+            ref_input=ref_input,
             ref_weight=ref_weight,
             ref_bias=ref_bias,
             **loss_kwargs,
@@ -301,6 +303,7 @@ def _compute_loss(
         beta=0.1,
         compute_nll_loss=True,
         use_ref_model=False,
+        ref_input=None,
         ref_weight=None,
         ref_bias=None,
         **loss_kwargs,
@@ -319,6 +322,7 @@ def _compute_loss(
             beta (float): Weight for the preference loss.
             compute_nll_loss (bool): Whether to compute NLL loss.
             use_ref_model (bool): Whether to use a reference model for the alignment loss.
+            ref_input (torch.Tensor): Reference input tensor. Shape: (2 * chunk_size, sequence_length, hidden_size).
             ref_weight (torch.Tensor): Reference weight tensor. Shape: (vocab_size, hidden_size).
             ref_bias (torch.Tensor, optional): Reference bias tensor. Shape: (vocab_size,).
             loss_kwargs (dict): Additional arguments for the loss function.
@@ -357,7 +361,7 @@ def _compute_loss(
                     ref_rejected_logits,
                     ref_chosen_nll_loss,
                 ) = LigerFusedLinearPreferenceBase.chunk_forward(
-                    input_chunk,
+                    ref_input,
                     ref_weight,
                     target_chunk,
                     ref_bias,