Optimizing Parallel and Distributed Reasoning Language Model Training

Recent advancements in Reasoning Language Models (RLMs), achieved through RL-style post-training methods like Reinforcement Learning with Verifiable Rewards (RLVR), have significantly enhanced capabilities such as chain-of-thought reasoning and self-correction. However, the computational demands for training these state-of-the-art RLMs are immense, often requiring millions of GPU-hours and complex multi-model pipelines that push current hardware limits. This research addresses the challenge of making RLM training high-performance, scalable, and cost-effective. It begins by systematizing the "RL-for-LLMs" paradigm and conducting a compute-centric analysis of prominent post-training algorithms, including Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO). Crucially, the paper introduces a comprehensive taxonomy of parallelism strategies applicable to RL-for-LLMs. This covers both traditional techniques like data, tensor, and pipeline parallelism, as well as novel approaches such as disaggregated placement, stage fusion, hybrid parallelism, and asynchronous execution, all rigorously analyzed using the work-depth model. The work concludes by distilling practical guidelines and outlining open research questions for developing efficient and scalable RLMs.