Research3h ago
Finbarr Timbers Breaks Down Post-Training Recipes
A detailed interview with Finbarr Timbers reveals the precise mechanics of frontier model post-training. He explains how RLHF and preference optimization refine raw base models into usable assistants. The discussion clarifies the specific trade-offs between stability and creativity. Practitioners gain a technical blueprint for scaling alignment without degrading a model's core reasoning capabilities.