Optimizing an Ensemble of Reward Models
Based on the scenario, analyze the fundamental weakness of using a simple average to combine the reward model scores. Propose a more principled aggregation strategy that accounts for model uncertainty, and explain precisely how this strategy would mitigate the problem of the chatbot producing factually incorrect responses.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Computing Sciences
Foundations of Large Language Models Course
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
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A team is developing a system that uses an ensemble of three different reward models to evaluate the helpfulness of AI-generated responses. For a particularly ambiguous user query, the models produce highly divergent scores: Model A gives 9/10, Model B gives 2/10, and Model C gives 5/10. The team wants to combine these scores into a single, reliable reward signal. Why would an aggregation method that weights each model's score based on its posterior probability be more effective in this situation than simply averaging the scores?
Applying Bayesian Model Averaging to Reward Models
Optimizing an Ensemble of Reward Models