Poster
in
Workshop: Instruction Tuning and Instruction Following
Reward Model Aggregation
Zihao Wang · Chirag Nagpal · Alexander D'Amour · Victor Veitch · Sanmi Koyejo
Keywords: [ LLM alignment ] [ reward aggregation ]
Abstract:
Aligning language models requires guiding outputs towards desired properties using reward models. This paper tackles the challenge of combining multiple reward models for diverse objectives. We introduce methods for aggregating these rewards using logical operations. Experiments confirm our methods beat traditional aggregation techniques and underscore the significance of proper reference values.
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