The traditional approach of evaluating language models based on final outcomes hampers understanding of specific errors and intermediate reasoning steps. Process supervision, introduced through process reward models, focuses on assessing the accuracy of each reasoning step to enhance model performance. This method involves freezing reasoning traces at various points and utilizing completer policies to generate completions, enabling a more granular evaluation of the model's reasoning process.
Our 171st episode with a summary and discussion of last week's big AI news!
With hosts Andrey Kurenkov (https://twitter.com/andrey_kurenkov) and Jeremie Harris (https://twitter.com/jeremiecharris)
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