6 papers · 1 filter
Filtered Direct Preference Optimization
Tetsuro Morimura, Mitsuki Sakamoto, Yuu Jinnai +2
Reinforcement learning from human feedback (RLHF) plays a crucial role in aligning language models with human preferences. While the significance of dataset quality is generally re…
Mean-Variance Efficient Reinforcement Learning with Applications to Dynamic Financial Investment
Masahiro Kato, Kei Nakagawa, Kenshi Abe +2
This study investigates the mean-variance (MV) trade-off in reinforcement learning (RL), an instance of the sequential decision-making under uncertainty. Our objective is to obtain…
Policy Gradient Algorithms with Monte Carlo Tree Learning for Non-Markov Decision Processes
Tetsuro Morimura, Kazuhiro Ota, Kenshi Abe +1
Policy gradient (PG) is a reinforcement learning (RL) approach that optimizes a parameterized policy model for an expected return using gradient ascent. While PG can work well even…
Reinforcement Learning for Edit-Based Non-Autoregressive Neural Machine Translation
Hao Wang, Tetsuro Morimura, Ukyo Honda +1
Non-autoregressive (NAR) language models are known for their low latency in neural machine translation (NMT). However, a performance gap exists between NAR and autoregressive model…
Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding
Yuu Jinnai, Ukyo Honda, Tetsuro Morimura +1
One of the most important challenges in text generation systems is to produce outputs that are not only correct but also diverse. Recently, Minimum Bayes-Risk (MBR) decoding has ga…
Model-Based Minimum Bayes Risk Decoding for Text Generation
Yuu Jinnai, Tetsuro Morimura, Ukyo Honda +2
Minimum Bayes Risk (MBR) decoding has been shown to be a powerful alternative to beam search decoding in a variety of text generation tasks. MBR decoding selects a hypothesis from…