Google Proposes New Technique to Improve Reliability and Alignment of AI Systems
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Google DeepMind published a paper proposing Weight Averaged Reward Models (WARM) to train AI systems so they provide reliable answers and avoid reward hacking.
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WARM averages together multiple small reward models to create a more robust overall model resistant to inconsistencies.
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WARM makes AI systems more reliable when dealing with shifting data distributions and inconsistencies in human feedback.
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WARM follows an updatable machine learning approach so models can adapt over time without starting over.
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WARM reduces but doesn't fully eliminate issues like biases; more research is needed but it's a step toward more aligned AI.