9 citations · 11 across the 7 of their papers we have counts for
6 papers · 1 filter
Safety Hacking in Constrained Best-of- Inference-time Scaling
Akifumi Wachi, Takumi Tanabe, Youhei Akimoto
Inference-time pipelines often sample multiple outputs, filter them with a learned safety model, and return the proxy-feasible output with the highest learned reward. We show that…
Sample-Efficient Hypergradient Estimation for Decentralized Bi-Level Reinforcement Learning
Mikoto Kudo, Takumi Tanabe, Akifumi Wachi +1
Many strategic decision-making problems, such as environment design for warehouse robots, can be naturally formulated as bi-level reinforcement learning (RL), where a leader agent…
Cost-Minimized Label-Flipping Poisoning Attack to LLM Alignment
Shigeki Kusaka, Keita Saito, Mikoto Kudo +3
Large language models (LLMs) are increasingly deployed in real-world systems, making it critical to understand their vulnerabilities. While data poisoning attacks during RLHF/DPO a…
A Provable Approach for End-to-End Safe Reinforcement Learning
Akifumi Wachi, Kohei Miyaguchi, Takumi Tanabe +2
A longstanding goal in safe reinforcement learning (RL) is a method to ensure the safety of a policy throughout the entire process, from learning to operation. However, existing sa…
Stepwise Alignment for Constrained Language Model Policy Optimization
Akifumi Wachi, Thien Q. Tran, Rei Sato +2
Safety and trustworthiness are indispensable requirements for real-world applications of AI systems using large language models (LLMs). This paper formulates human value alignment…
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification
Takumi Tanabe, Rei Sato, Kazuto Fukuchi +2
In the field of reinforcement learning, because of the high cost and risk of policy training in the real world, policies are trained in a simulation environment and transferred to…