Both a standard generative language model and an RLHF reward model are often based on the same core architecture (e.g., a Transformer decoder). What is the key architectural modification that allows the reward model to produce a single scalar quality score for a given text, rather than generating a new sequence of text?
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
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Ch.4 Alignment - Foundations of Large Language Models
Analysis in Bloom's Taxonomy
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Both a standard generative language model and an RLHF reward model are often based on the same core architecture (e.g., a Transformer decoder). What is the key architectural modification that allows the reward model to produce a single scalar quality score for a given text, rather than generating a new sequence of text?
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