A three-stage progressive training strategy -- large-scale pre-training, hard sample fine-tuning, and GRPO alignment -- sequentially exploits these data at different quality tiers.
大多数人认为训练策略应该统一应用于所有数据，但作者提出了分阶段渐进式训练策略，在不同质量层级的数据上采用不同方法，这种针对数据质量差异的训练方法挑战了传统'一刀切'的训练范式，代表了数据为中心的AI新思路。
SOTA models of different architectures and parameter scales exhibit highly consistent failure patterns on the same set of hard samples, suggesting that the performance bottleneck stems from shared deficiencies in training data rather than architecture itself.
大多数人认为不同架构的模型会有不同的失败模式和弱点，但作者发现无论架构和参数规模如何，SOTA模型在相同困难样本上表现出高度一致的失败模式，这表明性能瓶颈源于训练数据的共同缺陷，而非架构差异，这一发现挑战了模型多样化的传统观点。
If we knew that every image uploaded was a beautiful model shot, segmentation would be far easier, but because of the nature of user-uploaded content, we need the best possible segmentation.
大多数人可能认为高质量的专业照片是AI图像处理的理想输入，但作者暗示即使是'完美'的模特照片实际上比用户上传的真实内容更容易处理。这一观点挑战了人们对'理想训练数据'的假设，暗示真实世界数据的'不完美'实际上构成了更严峻的技术挑战。
AI models collapse when trained on recursively generated data by Ilia Shumailov et al.
ᔥ[[Mathew Lowry]] in AI4Communities post - MyHub Experiments Wiki (accessed:: 2024-11-06 09:43:23)
the model collapse paper now suggests that the training data created by well-managed communities could be the new currency of collective intelligence.
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Banerjee, A. (2022, January 12). I’m leading a long Covid trial – it’s clear Britain has underestimated its impact. The Guardian. https://www.theguardian.com/commentisfree/2022/jan/12/long-covid-trial-britain-short-term-virus
ORWG Virtual Meeting 08/09/2020 https://www.youtube.com/playlist?list=PLOA0aRJ90NxvXtMt5Si5ukmR9LYfvDueB (n.d.)
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