SFT with sampling for post-training: claims on generalization and forgetting
A post describes adding sampling to the post-training stack and claims SFT can compete with current methods, with better generalization and less forgetting in some cases.
Radar records a post describing the addition of sampling to the post-training stack. According to the text, the authors claim SFT can become competitive with current post-training methods, with better generalization and less forgetting in some cases. The available summary gives no benchmark figures, experimental conditions or method details, so these are the authors' claims and not results verified here.
The relevance lies with engineers who evaluate SFT and RL for post-training and may investigate the alleged differences in generalization and forgetting. Rota Nacional does not implement this method or guarantee its results. To study the topic with AI, do not paste CPFs, e-mail addresses, phone numbers or credentials into prompts; the platform applies the protection policy before inference, but the safest approach is not to send the data at all. Check the original to validate the details.