Unverified50% confidenceFactExact time
Traditional draft models are trained on narrow distributions targeting a single model, causing acceptance rates to drop sharply when workloads change, and requiring the entire pre-training process to be redone each time a target model is replaced.
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9/11/2026
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UnverifiedAfter large models entered the post-pretraining era, the capability gains from simply stacking parameters have become increasingly diminishing, and vendors have turned to the post-training phase in search of breakthroughs.77% similarUnverifiedThe core process of training a model is to iteratively update parameters in the direction of the loss function's gradient via gradient descent.77% similarUnverified训练-部署鸿沟指模型因训练与部署环境的数据分布偏移、软件依赖版本差异或硬件指令集不同而导致的性能下降77% similarUnverified对抗图案通常针对特定模型、特定版本训练，更换检测算法或升级模型权重后可能完全失效75% similarUnverifiedSimply stacking parameters without sufficient training data results in a model that is large but not capable; a leap in parameters must be accompanied by a proportionally expanded training corpus and computing power to realize capability gains.74% similar
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