However, experts express skepticism about the practical utility of inference-time search. They argue that it is most effective when there is a clear evaluation function, which is not the case for many queries. Critics like Matthew Guzdial and Mike Cook highlight that this method does not improve the reasoning capabilities of AI models but rather circumvents their limitations. They note that while inference-time search might reduce errors by checking multiple attempts, it may not be suitable for general language interactions or complex problem-solving, leaving the AI industry still searching for more efficient scaling techniques.
Key takeaways:
- Researchers have proposed a new AI scaling law called "inference-time search," which involves generating multiple answers to a query and selecting the best one.
- Inference-time search can enhance the performance of older models, like Google's Gemini 1.5 Pro, to surpass newer models on certain benchmarks.
- Experts are skeptical about the practicality of inference-time search, noting it requires a clear evaluation function and may not be suitable for general language interaction.
- The AI industry continues to seek new scaling techniques to improve model reasoning in a compute-efficient manner, as current methods can be costly.