Consider two distinct approaches for optimizing a language model's policy based on human preferences.

Approach A operates under the core assumption that both the underlying preference model and the initial reference policy are static and do not change during training. The optimization process focuses exclusively on updating the target policy being trained.

Approach B involves a more dynamic process where the policy is updated incrementally, and the system may involve components that are not held constant throughout training.

Analyze the primary trade-off introduced by Approach A's core assumption. Discuss the implications of this assumption on the complexity and stability of the training process when compared to a more dynamic approach like Approach B.