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Google DeepMind's New LLM Approach Outperforms Larger Models

Aug 7, 2024, 01:54 AM

Google DeepMind and UC Berkeley researchers have released a paper demonstrating that optimizing test-time computation for large language models (LLMs) can be more effective than merely increasing model parameters. This approach allows LLMs to outperform models that are 14 times larger by utilizing more computation during inference. The research highlights a significant advancement in the development of self-improving LLMs, marking a critical step towards more efficient AI systems.