Information‑gain design trims training compute

Amortizing sequential Bayesian experimental design turns the expensive, inference‑time search for information gain into a cheap forward pass, letting LLMs ask smarter questions with far less compute. The shift from on‑the‑fly optimization to a learned policy reshapes how we think about interactive dialogue systems. Previously, adaptive query... Weiterlesen: Information‑gain design trims training compute