Google AI Proposes PERL: A Parameter Efficient Reinforcement Learning Technique that can Train a Reward Model and RL Tune a Language Model Policy with LoRA

Reinforcement Learning from Human Feedback (RLHF) enhances the alignment of Pretrained Large Language Models (LLMs) with human values, improving their applicability and reliability. However, aligning LLMs through RLHF faces significant hurdles, primarily due to the process’s computational intensity and resource demands. Training LLMs with RLHF is a complex, resource-intensive task that limits its widespread adoption.  […]

The post Google AI Proposes PERL: A Parameter Efficient Reinforcement Learning Technique that can Train a Reward Model and RL Tune a Language Model …

ai paper summary ai shorts alignment applications artificial intelligence editors pick feedback google however human human feedback improving language language model language models large language large language models llms lora machine learning perl policy reinforcement reinforcement learning reliability reward model rlhf staff tech news technology through train values