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Developers have begun seeking new AI training methods as recent models have started to see shrinking returns. According to recent reports, models created within top AI labs are experiencing slower improvement rates than they used to, prompting developers to question the validity of AI scaling laws for model training. Improving model training means rethinking the guiding strategies surrounding our understanding of artificial intelligence model enhancement.
For the past five years, developers operated under the belief that pretraining models using more compute and data would produce more powerful AI capabilities. However, recent reports point to slower improvement rates for AI models, even from top AI labs. Google’s AI model Gemini failed to achieve performance gains, and Anthropic experienced development issues that prompted a delay in the release of its Claude 3.5 Opus AI model. Similarly, OpenAI’s Orion model isn’t meeting performance expectations regarding coding improvements and saw minimal new enhancements compared to previous models.
With models showing declining enhancement rates and diminished returns, AI developers could be forced to enter a new age of model training methods.
The Problem With AI Scaling Laws
Recent AI model developments show that AI labs can’t rely solely on applying more data and computation to produce more enhanced models. This poses a challenge for labs that have used traditional AI scaling laws as a leading factor in their model development operations. While the initial versions of AI models produced by developers like OpenAI, Meta, Anthropic, and Google had improved by combining more GPUs and larger data quantities, these methods alone cannot sustain exponential growth.
AI scaling laws had also previously contributed to expectations for the technology’s seemingly endless enhancement potential. Top AI companies have made significant investments in model development and made optimistic claims regarding AI’s future that reflect the anticipated returns granted from these technologies. In response, developers are applying new training strategies to remain competitive as we enter the next era of AI scaling laws.
New Training Strategies To Pick Up The Pace
AI labs are working quickly to improve models through new scaling methods. Recently favored strategies include “test-time compute,” a training technique that gives models more time to compute to produce output to considered questions. A recent paper published by MIT researchers shows that test-time compute substantially improves model performance on reasoning tasks.
Developers will likely continue to use traditional methods, such as applying more relevant datasets and compute clusters. However, the demand for fast AI interference chips could increase if the time-test compute strategy becomes the next leading training method. Whatever the future of AI development may be, the overall buzz around its potential remains positive as AI labs compete to produce the next best thing in the AI model scene.