Reinforcement learning from human feedback (RLHF)  can improve the quality of large language model’s (LLM)  outputs by aligning them with human preferences. We propose a simple algorithm for aligning LLMs with human preferences inspired by growing batch reinforcement learning (RL) , which we callReinforced Self-Training(\rest) . Given an initial LLM policy,\restproduces a dataset by generating samples from the policy, which are then used to improve the LLM policy using offline RL algorithms.\restis more efficient than typical online RLHF methods because the training dataset is produced offline, which allows data reuse. While\restis a general approach applicable to all generative learning settings, we focus on its application to machine translation. Our results show that\restcan substantially improve translation quality, as measured by automated metrics and human evaluation on machine translation benchmarks in a compute and sample-efficient manner.