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Unit Reward Function for Segments
A simplified reward function can be implemented where the reward for any given segment is a constant value of 1. This is formally expressed as . In this model, the reward is independent of the prompt , the complete response , and the specific content of the segment .
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Computing Sciences
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Unit Reward Function for Segments
Reward Model Loss Calculation
A reward model is being trained to score segments of a generated text. The training objective is to maximize a loss function defined as the negative mean squared error between the model's predicted scores and the provided target scores for each segment. If, during training, the calculated loss for a batch of segments is a value very close to zero (e.g., -0.001), what does this indicate about the model's performance on that specific batch?
Behavior of the Rating Loss Function