Unverified50% confidenceOpinionExact time
When a base model's general capabilities already cover business needs, the marginal returns of post-training decline rapidly.
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50%
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Long-term
Relevance
11/2/2026
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Related Claims
UnverifiedTraditional model capability scaling relies on investing more data and parameters during the training phase (scaling law), but the marginal returns of this approach are diminishing.79% similarUnverifiedTraditional 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.78% similarVerifiedDuring the post-training phase, an excessively low loss may correspond to overfitting, where the model memorizes the training data and loses generalization ability.78% similarUnverifiedAfter 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.78% similarUnverifiedIf pretraining efficiency improves by 10x, it would significantly lower the barrier to training frontier models, enabling more small and medium-sized teams, research institutions, and even independent developers to participate in foundational model development.77% similar
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