Robustness is often regarded as a critical future challenge for real-world applications, where stability is essential. However, as models often learn tasks in a similar order, we hypothesize that easier tasks will be easier regardless of how they are presented to the model. Indeed, in this paper, we show that as models approach high performance on a task, robustness is effectively achieved. Through an empirical analysis of multiple models across diverse datasets and configurations (e.g., paraphrases, different temperatures), we find a strong positive correlation. Moreover, we find that robustness is primarily driven by task-specific competence rather than inherent model-level properties, challenging current approaches that treat robustness as an independent capability. Thus, from a high-level perspective, we may expect that as new tasks saturate, model robustness on these tasks will emerge accordingly. For researchers, this implies that explicit efforts to measure and improve robustness may warrant reduced emphasis, as such robustness is likely to develop alongside performance gains. For practitioners, it acts as a sign that indeed the tasks that the literature deals with are unreliable, but on easier past tasks, the models are reliable and ready for real-world deployment.

翻译：鲁棒性常被视为现实世界应用中的关键未来挑战，稳定性在其中至关重要。然而，由于模型通常以相似的顺序学习任务，我们假设较简单的任务无论以何种方式呈现给模型都会更容易掌握。事实上，本文中我们证明，当模型在某一任务上接近高性能时，鲁棒性实际上已有效达成。通过对多种模型在不同数据集和配置（如释义、不同温度参数）下的实证分析，我们发现二者存在强正相关性。此外，我们发现鲁棒性主要由任务特定能力驱动，而非固有的模型层面属性，这对当前将鲁棒性视为独立能力的处理方法提出了挑战。因此，从高层视角看，我们可以预期随着新任务趋于饱和，模型在这些任务上的鲁棒性将相应涌现。对研究者而言，这意味着显式测量和改进鲁棒性的努力可能需要降低重视程度，因为此类鲁棒性很可能伴随性能提升自然形成。对实践者而言，这标志着文献所探讨的任务确实存在不可靠性，但在较简单的既往任务上，模型是可靠且已具备实际部署条件的。