Exponential reward weighting with a tuned temperature improves offline generative recommenders, and a new theory decomposes its suboptimality into coverage and noise costs that predict the observed inverted-U in performance.
2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning , pages=
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Exponential Reward Weighting for Fine-Tuning Generative Recommenders under Sparse and Noisy Feedback
Exponential reward weighting with a tuned temperature improves offline generative recommenders, and a new theory decomposes its suboptimality into coverage and noise costs that predict the observed inverted-U in performance.