diff --git a/src/art/unsloth/train.py b/src/art/unsloth/train.py
index ddacaafd..edd1e2d0 100644
--- a/src/art/unsloth/train.py
+++ b/src/art/unsloth/train.py
@@ -24,6 +24,13 @@ async def train(
     trainer: "GRPOTrainer",
     results_queue: asyncio.Queue[dict[str, float]],
 ) -> None:
+    # Disable xformers to force SDPA path for custom attention bias support
+    # xformers with GQA (5D tensors) doesn't support custom bias during gradient checkpointing
+    import unsloth.models.mistral as mistral_module
+
+    _has_xformers = getattr(mistral_module, "HAS_XFORMERS", False)
+    mistral_module.HAS_XFORMERS = False
+
     _compute_loss = trainer.compute_loss
     _log = trainer.log
     trainer.compute_loss = get_compute_loss_fn(trainer)
@@ -41,6 +48,7 @@ async def train(
     finally:
         trainer.compute_loss = _compute_loss
         trainer.log = _log
+        mistral_module.HAS_XFORMERS = _has_xformers
 
 
 def get_compute_loss_fn(trainer: "GRPOTrainer") -> Callable[..., torch.Tensor]:
@@ -97,7 +105,16 @@ def compute_loss(
             dtype_for_autocasting = torch.bfloat16
 
         batch_size, seq_len = inputs["tokens"].size()
-        attn_bias = calculate_attn_bias(
+        # Get attention head counts from model config for xformers format
+        # Training mode (requires_grad=True): 4D [B, n_heads, S, S]
+        # Inference mode (requires_grad=False): 5D [B, n_kv_heads, n_groups, S, S]
+        model_config = trainer.model.config  # type: ignore[union-attr]
+        num_attention_heads = int(model_config.num_attention_heads)  # type: ignore[union-attr]
+        num_key_value_heads = int(
+            getattr(model_config, "num_key_value_heads", num_attention_heads)
+        )
+        # Create base 3D mask, will be expanded in calculate_logprobs
+        attn_bias_3d = calculate_attn_bias(
             batch_size,
             seq_len,
             trainer.accelerator.device,
@@ -127,7 +144,7 @@ def compute_loss(
             dtype_for_autocasting,
             trainer,
             inputs["tokens"],
-            attn_bias,
+            attn_bias_3d,
             forward_kwargs,
             next_input_ids,
             lm_head_t,
@@ -135,6 +152,8 @@ def compute_loss(
             inference_mode=return_new_logprobs,
             no_grad=return_new_logprobs,
             reference_logprobs=False,
+            num_attention_heads=num_attention_heads,
+            num_key_value_heads=num_key_value_heads,
         )
         if return_new_logprobs:
             return torch.nn.functional.pad(new_logprobs[:, :-1], (1, 0), value=0.0)
@@ -143,7 +162,7 @@ def compute_loss(
                 dtype_for_autocasting,
                 trainer,
                 inputs["tokens"],
-                attn_bias,
+                attn_bias_3d,
                 forward_kwargs,
                 next_input_ids,
                 lm_head_t,
@@ -151,10 +170,12 @@ def compute_loss(
                 inference_mode=True,
                 no_grad=False,
                 reference_logprobs=True,
+                num_attention_heads=num_attention_heads,
+                num_key_value_heads=num_key_value_heads,
             )
         else:
             ref_logprobs = None
-        del attn_bias
+        del attn_bias_3d
 
         loss = loss_fn(
             inputs,
@@ -204,6 +225,11 @@ def calculate_attn_bias(
     parent_ids: torch.Tensor,
     dtype: torch.dtype,
 ) -> torch.Tensor:
+    """Calculate base 3D attention bias [B, S, S] from group/parent IDs.
+
+    The bias is expanded to the appropriate format (4D or 5D) in calculate_logprobs
+    based on whether we're in inference mode or training mode.
+    """
     mask = calculate_mask(batch_size, seq_len, device, group_ids, parent_ids)
     # Use the same dtype as autocast to save memory and avoid dtype conversions
     attn_bias = torch.where(
@@ -260,9 +286,21 @@ def calculate_logprobs(
     inference_mode: bool,
     no_grad: bool,
     reference_logprobs: bool,
+    num_attention_heads: int,
+    num_key_value_heads: int,
 ) -> tuple[
     torch.Tensor, torch.Tensor
 ]:  # Returns (log_probs, entropy) both shape [B, S]
+    # Expand 3D causal_mask [B, S, S] to 4D [B, n_heads, S, S] for SDPA
+    # We disable xformers in the train() function to force the SDPA path
+    # because xformers with GQA doesn't support custom bias during gradient checkpointing
+    batch_size, seq_len, _ = causal_mask.shape
+    expanded_mask = (
+        causal_mask.unsqueeze(1)
+        .expand(batch_size, num_attention_heads, seq_len, seq_len)
+        .contiguous()
+    )
+
     with (
         torch.inference_mode() if inference_mode else nullcontext(),
         torch.no_grad() if no_grad else nullcontext(),
@@ -276,8 +314,9 @@ def calculate_logprobs(
         torch.amp.autocast_mode.autocast(device_type="cuda", dtype=dtype_for_autocast),
     ):
         hidden_states = trainer.model(  # type: ignore
-            input_ids=input_ids, causal_mask=causal_mask, **forward_kwargs
+            input_ids=input_ids, causal_mask=expanded_mask, **forward_kwargs
         ).logits  # Shape [B, S, H]
+    del expanded_mask
     return _calculate_logprobs(lm_head_t, hidden_states, next_input_ids, chunk_size)