IEEE

Reinforcement Learning-powered Effectiveness and Efficiency Few-shot Jailbreaking Attack LLMs

Abstract: The widespread use of large language models (LLMs) has brought about security risks, including biases, discrimination, and ethical concerns. Reinforcement Learning from Human Feedback (RLHF) ...
GitHub

mll-lab-nu/VAGEN

Kangrui Wang*, Pingyue Zhang*, Zihan Wang*, Yaning Gao*, Linjie Li*, Qineng Wang, Hanyang Chen, Chi Wan, Yiping Lu, Zhengyuan Yang, Lijuan Wang, Ranjay Krishna, Jiajun Wu, Li Fei-Fei, Yejin Choi, ...
techxplore

Why reinforcement learning breaks at scale, and how a new method fixes it

From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have an important impact. That may feel especially true, for example, when ...
marktechpost

A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a ...
IEEE

Spiking Variational Policy Gradient for Brain Inspired Reinforcement Learning

Abstract: Recent studies in reinforcement learning have explored brain-inspired function approximators and learning algorithms to simulate brain intelligence and adapt to neuromorphic hardware. Among ...