Industrial recommendation systems process billions of daily user feedback which are complex and noisy. Efficiently uncovering user preference from these signals becomes crucial for high-quality recommendation. We argue that those signals are not inherently equal in terms of their informative value and training ability, which is particularly salient in industrial applications with multi-stage processes (e.g., augmentation, retrieval, ranking). Considering that, in this work, we propose a novel self-auxiliary distillation framework that prioritizes training on high-quality labels, and improves the resolution of low-quality labels through distillation by adding a bilateral branch-based auxiliary task. This approach enables flexible learning from diverse labels without additional computational costs, making it highly scalable and effective for Google-scale recommenders. Our framework consistently improved both offline and online key business metrics across three Google major products. Notably, self-auxiliary distillation proves to be highly effective in addressing the severe signal loss challenge posed by changes such as Apple iOS policy. It further delivered significant improvements in both offline (+17\% AUC) and online metrics for a Google Apps recommendation system. This highlights the opportunities of addressing real-world signal loss problems through self-auxiliary distillation techniques.