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…
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- 2026-11-09 06:00 · DEV Community — Machine Learning
Information‑gain design trims training compute