diff --git a/src/tinker/lib/public_interfaces/training_client.py b/src/tinker/lib/public_interfaces/training_client.py
index d99a90a..d0cb768 100644
--- a/src/tinker/lib/public_interfaces/training_client.py
+++ b/src/tinker/lib/public_interfaces/training_client.py
@@ -263,14 +263,32 @@ async def forward_backward_custom_async(
         import torch
 
         # First do a forward pass and get logprobs
-        forward_future = await self.forward_async(data, "cross_entropy")
+        # -> self.forward_async(data, loss_fn="cross_entropy") expects data to
+        #    be Datum list with loss_fn_inputs containing "target_tokens" and "weights"
+        def convert_to_cross_entropy_datum(datum: types.Datum) -> types.Datum:
+            """
+            Remove non-cross_entropy keys from loss_fn_inputs in tinker Datum
+            """
+            _keys = ["target_tokens", "weights"]
+            # Add weights to loss_fn_inputs
+            # - Following https://github.com/thinking-machines-lab/tinker-cookbook/blob/5ae76f38111ec97e476b5f95b43903e675208b52/tinker_cookbook/rl/data_processing.py#L165
+            #   use advantage == 0 as a heuristic for tokens to ignore (weights = 0)
+            # - Also pass PyTorch tensor directly (will be converted to TensorData)
+            datum.loss_fn_inputs["weights"] = torch.ones_like(
+                datum.loss_fn_inputs["advantages"].to_torch() != 0  # type: ignore
+            )
+            loss_fn_inputs = {k: v for k, v in datum.loss_fn_inputs.items() if k in _keys}
+            return types.Datum(model_input=datum.model_input, loss_fn_inputs=loss_fn_inputs)
+
+        _data = list(map(convert_to_cross_entropy_datum, data))
+        forward_future = await self.forward_async(_data, "cross_entropy")
         forward_result = await forward_future.result_async()
         logprobs_list: List[torch.Tensor] = []
         for out in forward_result.loss_fn_outputs:
             logprob = torch.tensor(out["logprobs"].data).clone().detach().requires_grad_(True)
             logprobs_list.append(logprob)
 
-        # Now apply user-provided function
+        # Now apply user-provided function (on original data list)
         loss, metrics = loss_fn(data, logprobs_list)
         loss.backward()
         grads = []
@@ -280,7 +298,7 @@ async def forward_backward_custom_async(
             grads.append(logprob.grad)
 
         linear_loss_data = []
-        for datum, grad in zip(data, grads):
+        for datum, grad in zip(data, grads, strict=True):
             loss_fn_inputs: Any = {
                 "target_tokens": datum.loss_fn_inputs["target_tokens"],
                 "weights": -grad,  # Pass PyTorch tensor directly (will be converted to TensorData)