This AI Paper Unveils the Secrets to Optimizing Large Language Models: Balancing Rewards and Preventing Overoptimization

A team of researchers from UC Berkeley, UCL, CMU, and Google Deepmind address the challenge of optimising large language models using composite reward models derived from various simpler reward models. These hybrid models often need help with the appropriate weighting of component models, leading to over-optimization, where higher reward correlates with worse human ratings. Their […]

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