Get the latest tech news
A new paradigm for AI: How ‘thinking as optimization’ leads to better general-purpose models
A new AI model learns to "think" longer on hard problems, achieving more robust reasoning and better generalization to novel, unseen tasks.
Applying this to AI reasoning, the researchers propose in a paper that devs should view “thinking as an optimization procedure with respect to a learned verifier, which evaluates the compatibility (unnormalized probability) between an input and candidate prediction.” The process begins with a random prediction, which is then progressively refined by minimizing its energy score and exploring the space of possible solutions until it converges on a highly compatible answer. “This aligns with our claims that because traditional feed-forward transformers cannot dynamically allocate additional computation for each prediction being made, they are unable to improve performance for each token by thinking for longer,” the researchers write. For developers and enterprises, the strong reasoning and generalization capabilities of EBTs could make them a powerful and reliable foundation for building the next generation of AI applications.
Or read this on Venture Beat
