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2025 ICML ICML 2025

Portable Reward Tuning: Towards Reusable Fine-Tuning across Different Pretrained Models

Abstract

No abstract available. No abstract was captured for this paper when its proceedings were scraped - most often an older edition published without one online. How the corpus is built.

Authors

Daiki Chijiwa, Taku Hasegawa, Kyosuke Nishida, Kuniko Saito, Susumu Takeuchi

Topics

Artificial Intelligence > Core AI > Foundation Models Machine Learning > Application Areas > Domain Adaptation Machine Learning > Learning Types > Transfer Learning

Keywords

reward model foundation model inference cost fine tuning kullback-leibler regularization portable reward tuning
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