DeepMind

In a significant leap for biological and health research, Google DeepMind announced AlphaProteo, a new AI-driven system designed to create novel protein binders with potential to revolutionize drug development, disease research, and biosensor development. Building on the success of AlphaFold, which predicts protein structures, AlphaProteo goes further by generating new proteins that can tightly bind to specific targets, an essential aspect of many biological processes.

In a significant leap for biological and health research, Google DeepMind announced AlphaProteo, a new AI-driven system designed to create novel protein binders with potential to revolutionize drug development, disease research, and biosensor development. Building on the success of AlphaFold, which predicts protein structures, AlphaProteo goes further by generating new proteins that can tightly bind to specific targets, an essential aspect of many biological processes.

Researchers from Google DeepMind, University of Toronto, MILA and UCLA have introduced a novel approach called Generative Reward Modeling (GenRM). This method redefines the verification process by framing it as a next-token prediction task, a fundamental capability of LLMs. Unlike traditional discriminative RMs, GenRM integrates the text-generation strengths of LLMs into the verification process, allowing the model to generate and evaluate potential solutions simultaneously. This approach also supports Chain-of-Thought (CoT) reasoning, where the model generates intermediate reasoning steps before arriving at a final decision. The GenRM method, therefore, not only assesses the correctness of solutions but also enhances the overall reasoning process by enabling more detailed and structured evaluations.

The GenRM methodology employs a unified training approach combining solution generation and verification. This is achieved by training the model to predict the correctness of a solution through next-token prediction, a technique that leverages the inherent generative abilities of LLMs. In practice, the model generates intermediate reasoning steps—CoT rationales—which are then used to verify the final solution. This process integrates seamlessly with existing AI training techniques, allowing for the simultaneous improvement of generation and verification capabilities. Furthermore, the GenRM model benefits from additional inference-time computation, such as majority voting aggregating multiple reasoning paths to arrive at the most accurate solution....

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