This research paper explores the potential for ‘jailbreak backdoors’ in large language models trained with Reinforcement Learning from Human Feedback (RLHF). It reveals that a malicious actor could potentially poison…
Continue readingThe paper presents a study on improving the security of Internet of Things (IoT) devices using artificial intelligence (AI) and reinforcement learning. It specifically focuses on the use of attack…
Continue readingThe article ‘Beyond Simulated Drivers: Evaluating the Impact of Real-World Car-Following in Mixed Traffic Control’ studies the effect of human driving behaviors on traffic congestion. It suggests that Human-driven Vehicles…
Continue readingThe paper presents a constraint-aware policy for compliant robotic manipulation, particularly in physically-constrained environments such as household operations. The policy, developed using reinforcement learning, is designed to adjust to the…
Continue readingThe article discusses the challenges of applying robotic manipulation in varied scenarios due to the limitations of control-based approaches and inefficiency of existing learning methods. To address these, the authors…
Continue readingThe paper proposes Interpretable Continuous Control Trees (ICCTs), a tree-based model that can be optimized using modern, gradient-based, reinforcement learning approaches to produce high-performing, interpretable policies. The ICCTs show potential…
Continue readingThe paper discusses the effectiveness of scalar feedback over binary feedback in reinforcement learning. While binary feedback has been more widely used due to its simplicity and reduced noise, scalar…
Continue readingThis article discusses the use of reinforcement learning to improve the mobility of quadruped robots on risky terrains. The researchers trained a generalist policy for agile locomotion on disorderly and…
Continue readingThis academic article discusses the security vulnerabilities in Reinforcement Learning with Human Feedback (RLHF) in Large Language Models (LLMs). RLHF plays a crucial role in aligning LLMs with human preferences….
Continue readingThe paper investigates the impact of preference agreement on the efficacy of Reinforcement Learning from Human Feedback (RLHF) in text summarization. The authors demonstrate that including a diverse range of…
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