Performance Foundations of Parallel and Distributed Reasoning Language Models (arxiv.org)

Recent advancements in Reinforcement Learning with Verifiable Rewards (RLVR) have significantly improved Reasoning Language Models (RLMs) like DeepSeek-R1 and Kimi k1.5 by enhancing their reasoning abilities and long-horizon planning. However, the training of these models demands enormous computational resources, often requiring millions of GPU-hours and complex, tightly integrated multi-model pipelines. This research highlights that effectively training RLMs is as much a challenge of parallel and distributed systems as it is of algorithmic development. To address these challenges, the authors present a comprehensive analysis of popular post-training frameworks such as Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), while introducing a new taxonomy of parallelism strategies tailored for RLMs. These strategies encompass both established techniques like data and tensor parallelism and innovative methods such as disaggregated placement and asynchronous execution. By leveraging a rigorous model of parallel computing, this work not only streamlines the development process for scalable and efficient RLMs but also sets the stage for future research directions aimed at optimizing costs and performance in RL-for-LLM training. This is a crucial step forward for the AI/ML community, paving the way for more accessible and powerful language model training.