The pattern is that if the main tasks have flattening learning curves (small negative gradients) in the 20-30% percentile, but the auxiliary task curves are still relatively steep, MTL is more likely to work.
multi-task gains are more likely for target tasks that quickly plateau with non-plateauing auxiliary tasks.