#variational-autoencoder

#variational-autoencoder

Continual learning is essential for foundation models; SDFT uses in-context learning to generate on-policy signals, avoiding explicit reward functions and reducing forgetting.

Continual learning enables foundation models to keep improving over time, and SDFT uses in-context demonstrations to generate on-policy signals without explicit rewards.