diff --git a/src/nnue/trainer/trainer_feature_transformer.h b/src/nnue/trainer/trainer_feature_transformer.h
index c883b594..77edfbde 100644
--- a/src/nnue/trainer/trainer_feature_transformer.h
+++ b/src/nnue/trainer/trainer_feature_transformer.h
@@ -285,6 +285,55 @@ namespace Eval::NNUE {
             const LearnFloatType local_learning_rate =
                 learning_rate * learning_rate_scale_;
 
+#if defined (USE_SSE2)
+            
+            {
+                static_assert(kHalfDimensions % 16 == 0, "This implementation assumes that it can process 16 floats at a time");
+
+                const __m128 kZero4 = _mm_set1_ps(+kZero);
+                const __m128 kOne4 = _mm_set1_ps(+kOne);
+
+                const IndexType total_size = batch_->size() * kOutputDimensions;
+
+                for (IndexType i = 0; i < total_size; i += 16)
+                {
+                    __m128 out0 = _mm_loadu_ps(&output_[i + 0]);
+                    __m128 out1 = _mm_loadu_ps(&output_[i + 4]);
+                    __m128 out2 = _mm_loadu_ps(&output_[i + 8]);
+                    __m128 out3 = _mm_loadu_ps(&output_[i + 12]);
+
+                    __m128 clipped0 = _mm_or_ps(_mm_cmple_ps(out0, kZero4), _mm_cmpge_ps(out0, kOne4));
+                    __m128 clipped1 = _mm_or_ps(_mm_cmple_ps(out1, kZero4), _mm_cmpge_ps(out1, kOne4));
+                    __m128 clipped2 = _mm_or_ps(_mm_cmple_ps(out2, kZero4), _mm_cmpge_ps(out2, kOne4));
+                    __m128 clipped3 = _mm_or_ps(_mm_cmple_ps(out3, kZero4), _mm_cmpge_ps(out3, kOne4));
+
+                    __m128 grad0 = _mm_loadu_ps(&gradients[i + 0]);
+                    __m128 grad1 = _mm_loadu_ps(&gradients[i + 4]);
+                    __m128 grad2 = _mm_loadu_ps(&gradients[i + 8]);
+                    __m128 grad3 = _mm_loadu_ps(&gradients[i + 12]);
+
+                    grad0 = _mm_andnot_ps(clipped0, grad0);
+                    grad1 = _mm_andnot_ps(clipped1, grad1);
+                    grad2 = _mm_andnot_ps(clipped2, grad2);
+                    grad3 = _mm_andnot_ps(clipped3, grad3);
+
+                    _mm_storeu_ps(&gradients_[i + 0], grad0);
+                    _mm_storeu_ps(&gradients_[i + 4], grad1);
+                    _mm_storeu_ps(&gradients_[i + 8], grad2);
+                    _mm_storeu_ps(&gradients_[i + 12], grad3);
+
+                    const int clipped_mask =
+                        (_mm_movemask_ps(clipped0) << 0)
+                        | (_mm_movemask_ps(clipped1) << 4)
+                        | (_mm_movemask_ps(clipped2) << 8)
+                        | (_mm_movemask_ps(clipped3) << 12);
+
+                    num_clipped_ += popcount(clipped_mask);
+                }
+            }
+
+#else
+
             for (IndexType b = 0; b < batch_->size(); ++b) {
                 const IndexType batch_offset = kOutputDimensions * b;
                 for (IndexType i = 0; i < kOutputDimensions; ++i) {
@@ -294,6 +343,9 @@ namespace Eval::NNUE {
                     num_clipped_ += clipped;
                 }
             }
+
+#endif
+
             num_total_ += batch_->size() * kOutputDimensions;
 
             // Since the weight matrix updates only the columns corresponding to the features that appeared in the input,