The world of artificial intelligence is ever-evolving, with a new study proposing 'inference-time search' as a method to significantly enhance AI capabilities. While this method brings hope, experts remain cautious about its potential to solve all AI performance challenges.
Decoding AI Scaling Laws
To understand the novelty of 'inference-time search,' it's crucial to comprehend the key AI scaling laws. These laws describe improvements in AI models with more data and computational power during training. Previously, pre-training on large datasets was dominant, but now attention has shifted to post-training and test-time scaling, allowing models to perform complex 'reasoning.'
Inference-Time Search: A Deep Dive into the New Method
The 'inference-time search' method involves an AI model generating multiple potential answers to a complex question and selecting the best one. Researchers claim this could significantly elevate even older models' performance. For instance, Google's Gemini 1.5 Pro could outperform OpenAI's model on challenging benchmarks using this technique. Eric Zhao of Google highlighted on social media that this method allows better results through simple self-verification of 200 responses without fine-tuning.
Skepticism and the Future of AI
Despite promising results, some experts urge caution, arguing the method isn't suitable for every scenario. The effectiveness of 'inference-time search' relies on a robust evaluation function. In areas where defining the 'best' answer is challenging, the method may falter. Mike Cook emphasizes it's a workaround to compensate AI's limitations, not enhancing reasoning capabilities.
'Inference-time search' offers intriguing exploration opportunities, but its limitations must be acknowledged, insisting on ongoing AI improvement research.
