Performance Foundations of Parallel & Distributed Reasoning Language Models

TL;DR - This paper frames reinforcement-learning post-training for reasoning language models as a parallel and distributed systems challenge. It provides a compute-centric analysis and practical framework for making RLM training faster, more scalable, and more cost-effective.

• Systematizes PPO, GRPO, and related RL-for-LLM post-training methods from a computational perspective.
• Develops a taxonomy spanning data, tensor, pipeline, sequence, context, and expert parallelism.
• Examines multi-model optimizations including disaggregated placement, stage fusion, hybrid parallelism, and asynchronous execution.
• Uses the work-depth model to analyze existing frameworks and derive practical guidance and open research directions.