Papers by Chiehyeon Lim
Towards Pareto-Efficient RLHF: Paying Attention to a Few High-Reward Samples with Reward Dropout (2024.findings-emnlp)
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| Challenge: | RLHF is a bi-objective problem that has the nature of a Pareto optimization . reward dropout is generalizable and most effective with non-pretrained target models . |
| Approach: | They propose a method that guarantees a Pareto improvement by leveraging reinforcement learning to fine-tune language models. |
| Outcome: | The proposed method guarantees a Pareto improvement on two benchmark datasets . it is generalizable and most effective with non-pretrained target models, saving the effort of pretraining. |