Overall, both multi-task learning and mixed fine-tuning show improved performance for ring-breaking disconnections while retaining the ability to predict other reaction classes, with mixed fine-tuning being preferable due to shorter training time. While the fine-tuned model performs best for heterocycle disconnections, it is not suitable for multi-step retrosynthesis due to catastrophic forgetting. Ensemble decoding ranks in the middle, not being as good at ring disconnections as the fine-tuned model, but also performing worse for other reaction classes than the mixed fine-tuned model. Due to this, we perform all further experiments and comparisons with the mixed fine-tuned model, as the most versatile and best performing one.