Integration Online Reinforcement Learning Loops in Language Model Training

Abstract

Online reinforcement learning (RL) loops have lately been added to augment Large Language Models (LLMs) so allowing constant improvement from feedback. Emphasizing scalable and adaptive methods, this paper reviews developing architectures and algorithms that include RL-based feedback into LLM training. We explore how conventional offline RL fine-tuning—best shown by Reinforcement Learning from Human Feedback, RLHF—has developed into online paradigms enabling models to learn in real time from interactions. From multi-stage training pipelines to new RL algorithms, we show fresh approaches that improve scalability and adaptability, so allowing LLMs to change with dynamic surroundings. These developments present difficulties in stability, safety, and efficiency even as they show notable improvements in language model performance, alignment, and generalization. We evaluate how online RL integrations enhance the responsiveness of LLMs to changing data and user needs; we also highlight unresolved issues and future directions for the deployment of adaptive LLMs in practical environments.