diff --git a/fast_llm/models/gpt/model.py b/fast_llm/models/gpt/model.py
index bb2f4ed5..d29fc28d 100644
--- a/fast_llm/models/gpt/model.py
+++ b/fast_llm/models/gpt/model.py
@@ -249,21 +249,28 @@ def preprocess(
                 TransformerKwargs.presents: presents,
             }
             if phase != PhaseType.inference:
+                sequence_offset = sequence_k - sequence_q + 1
                 if sequence_first:
-                    labels = batch.token_ids[sequence_k - sequence_q + 1 : sequence_k + 1]
+                    labels = batch.token_ids[sequence_offset : sequence_k + 1]
                 else:
                     # TODO: Avoid multiple contiguous calls?
                     labels = batch.token_ids[:, sequence_k - sequence_q + 1 : sequence_k + 1].contiguous()
                     # We set label indices to -100 for masked spans, inline with ignore_index in torch.nn.CrossEntropyLoss
                     # TODO: take ignore_index from config
-                    if batch.loss_masking_spans is not None:
-                        for i, spans_i in enumerate(batch.loss_masking_spans):
-                            mask_indices = (
-                                torch.cat([torch.arange(s - 1, e) for s, e in spans_i])
-                                if len(spans_i)
-                                else torch.tensor([], dtype=torch.int64)
-                            )
-                            labels[i, mask_indices] = -100
+                if batch.loss_masking_spans is not None:
+                    for i, spans in enumerate(batch.loss_masking_spans):
+                        if not spans.numel():
+                            continue
+                        valid_spans = spans[(spans[:, 0] <= sequence_k) & (spans[:, 1] >= sequence_offset)]
+                        if valid_spans.numel():
+                            valid_spans[:, 0].clamp_(min=sequence_offset)
+                            valid_spans[:, 1].clamp_(max=sequence_k)
+                            valid_spans -= sequence_offset
+                            for start, end in valid_spans:
+                                if sequence_first:
+                                    labels[start : end + 1, i] = -100
+                                else:
+                                    labels[i, start : end + 1] = -100
                 kwargs[LanguageModelKwargs.labels] = labels
             if self._config.use_absolute_position_embeddings:
                 self._position_embedding_preprocessor.preprocess(kwargs)