Unverified50% confidenceFactExact time
Models of the same size obtained by pruning from a larger model can often achieve comparable or even better performance with fewer training tokens.
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Long-term
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9/5/2026
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VerifiedThrough higher-quality training data, more optimized training strategies, and more refined post-training alignment, smaller-scale models can fully surpass larger-scale but lower-quality-trained models on practical tasks.78% similarUnverifiedThe paper found that for a fixed compute budget, training a smaller model for longer tends to yield better results than training a larger model, which contradicts the Chinchilla law.70% similarUnverifiedSpecialized super-resolution models carefully trained for specific domains often outperform general-purpose models.70% similarUnverifiedFine-tuning typically requires only one percent or less of the original training cost to achieve domain performance close to that of a dedicated model.70% similarUnverifiedUsing small models for skill distillation and large models for actual execution may be a more optimal cost-performance combination70% similar
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