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cs.LG2025
Test-time reward-guided alignment of language models by importance sampling on pre-logit space
Sekitoshi Kanai, Tsukasa Yoshida, Hiroshi Takahashi +2
Test-time alignment of large language models (LLMs) attracts attention because fine-tuning of LLMs requires high computational costs. In this paper, we propose a new test-time rewa…
cs.LG2024
Flipping-based Policy for Chance-Constrained Markov Decision Processes
Xun Shen, Shuo Jiang, Akifumi Wachi +2
Safe reinforcement learning (RL) is a promising approach for many real-world decision-making problems where ensuring safety is a critical necessity. In safe RL research, while expe…