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Researchers have proposed a new AI scaling law called inference-time search, which suggests that generating multiple answers in parallel can enhance model performance. This method reportedly allows Google's Gemini 1.5 Pro to outperform OpenAI's o1-preview model on certain benchmarks. However, experts express skepticism about its practical utility in various scenarios.
The concept of scaling laws in AI has evolved, with pre-training being the traditional approach, now complemented by post-training and test-time scaling. Despite the promising results from inference-time search, experts warn that it may not be effective for all types of queries, particularly those lacking clear evaluation criteria. The ongoing search for efficient scaling techniques highlights the challenges faced by the AI industry.
• New AI scaling law proposed but met with skepticism from experts.
• Inference-time search claims to enhance model performance significantly.
Scaling laws describe how AI model performance improves with larger datasets and computing resources.
Inference-time search involves generating multiple answers in parallel to select the best one.
Post-training scaling focuses on tuning a model's behavior after initial training.
5 Pro.
OpenAI develops advanced AI models, including the o1-preview, which is compared against Google's models.
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