Process-Based Reward Model as a Classification Task
A reward model can be trained on step-level annotated data to provide supervisory feedback for policy learning by treating the task as a classification problem. The model, typically a Transformer decoder with a final Softmax layer, takes the problem description () and the preceding reasoning steps () as input at step . It then outputs a probability distribution over a set of predefined labels, such as or , to classify the current step.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.4 Alignment - Foundations of Large Language Models
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Process-Based Reward Model as a Classification Task
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When a process-based reward model is framed as a classification task, its primary function is to output a single, continuous score (e.g., from 0.0 to 1.0) that represents the quality of a given reasoning step.