4 papers
Pruning Cannot Hurt Robustness: Certified Trade-offs in Reinforcement Learning
James Pedley, Benjamin Etheridge, Stephen J. Roberts +1
Reinforcement learning (RL) policies deployed in real-world environments must remain reliable under adversarial perturbations. At the same time, modern deep RL agents are heavily o…
Doubly Robust Alignment for Large Language Models
Erhan Xu, Kai Ye, Hongyi Zhou +3
This paper studies reinforcement learning from human feedback (RLHF) for aligning large language models with human preferences. While RLHF has demonstrated promising results, many…
Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning
Kai Ye, Hongyi Zhou, Jin Zhu +2
Reinforcement learning from human feedback (RLHF) has emerged as a key technique for aligning the output of large language models (LLMs) with human preferences. To learn the reward…
When Should We Orchestrate Multiple Agents?
Umang Bhatt, Sanyam Kapoor, Mihir Upadhyay +6
Strategies for orchestrating the interactions between multiple agents, both human and artificial, can wildly overestimate performance and underestimate the cost of orchestration. W…