diff --git a/sub-packages/bionemo-geneformer/src/bionemo/geneformer/scripts/train_geneformer.py b/sub-packages/bionemo-geneformer/src/bionemo/geneformer/scripts/train_geneformer.py
index 2fa9e4a68f..646f88feaa 100644
--- a/sub-packages/bionemo-geneformer/src/bionemo/geneformer/scripts/train_geneformer.py
+++ b/sub-packages/bionemo-geneformer/src/bionemo/geneformer/scripts/train_geneformer.py
@@ -25,6 +25,7 @@
 from pathlib import Path
 from typing import Dict, List, Optional, Sequence, Type, get_args
 
+import torch
 from megatron.core.distributed import DistributedDataParallelConfig
 from megatron.core.optimizer import OptimizerConfig
 from nemo import lightning as nl
@@ -91,6 +92,7 @@ def main(
     log_every_n_steps: int = 50,
     gc_interval: int = 0,
     aligned_megatron_ddp: bool = False,
+    recompilation_check: bool = False,
     # TODO add datamodule class, and ability to change data step to get full support for pretraining workflows
 ) -> None:
     """Train a Geneformer model on single cell data.
@@ -143,6 +145,8 @@ def main(
             at this requested interval of train/val steps. This will likely slow down single GPU runs.
         aligned_megatron_ddp (bool): if activated, this will activate a number of communication optimizations that are
             good for clusters. This will likely slow down single node runs though.
+        recompilation_check (bool): enable a recompilation check (only do on a small run) to verify that fused gpu
+            kernels are not being regularly recompiled, which is very expensive, with a particular model/settings.
     """
     # Create the result directory if it does not exist.
     result_dir.mkdir(parents=True, exist_ok=True)
@@ -273,6 +277,8 @@ def main(
         ffn_hidden_size=512,
         num_attention_heads=4,
         seq_length=seq_length,
+        bias_dropout_fusion=False,
+        bias_activation_fusion=False,
         params_dtype=get_autocast_dtype(precision),
         pipeline_dtype=get_autocast_dtype(precision),
         autocast_dtype=get_autocast_dtype(precision),  # setting this speeds things up a lot
@@ -325,6 +331,11 @@ def main(
         wandb_config=wandb_options,
         ckpt_callback=checkpoint_callback,
     )
+    if recompilation_check:
+        """This is _very_ useful for debugging slow forward passes. Check that your fused kernels are not
+        getting recompiled. Once verified, turn this off again.
+        """
+        torch._dynamo.config.error_on_recompile = True
     llm.train(
         model=model,
         data=data,
@@ -599,6 +610,13 @@ def config_class_type(desc: str) -> Type[BioBertConfig]:
         help="By default param overlap/etc is disabled in megatron, this enables all of those settings. This is probably "
         "good for cluster performance.",
     )
+    parser.add_argument(
+        "--recompilation-check",
+        action="store_true",
+        default=False,
+        help="Activate this and make sure a small training loop runs, this tells you that your settings are not "
+        "triggering regular recompilations which can be very expensive for fused gpu kernels.",
+    )
 
     return parser
 
@@ -648,6 +666,7 @@ def entrypoint():
         log_every_n_steps=args.log_every_n_steps,
         gc_interval=args.gc_interval,
         aligned_megatron_ddp=args.aligned_megatron_ddp,
+        recompilation_check=args.recompilation_check,
     )
 
 
diff --git a/sub-packages/bionemo-llm/src/bionemo/llm/model/loss.py b/sub-packages/bionemo-llm/src/bionemo/llm/model/loss.py
index 7e92fff658..04310aeabf 100644
--- a/sub-packages/bionemo-llm/src/bionemo/llm/model/loss.py
+++ b/sub-packages/bionemo-llm/src/bionemo/llm/model/loss.py
@@ -259,7 +259,7 @@ def forward(
             return loss_for_microbatch * cp_size, {"avg": reduced_loss}
 
 
-def unreduced_token_loss_fn(logits: Tensor, labels: Tensor, cross_entropy_loss_fusion: bool = True) -> Tensor:
+def unreduced_token_loss_fn(logits: Tensor, labels: Tensor, cross_entropy_loss_fusion: bool = False) -> Tensor:
     """Computes the unreduced token loss given the logits and labels without regard to the loss mask.
 
     WARNING: This function does not apply a loss mask. Also, it does inplace operation on the inputs.