Performance Foundations of Parallel & Distributed Reasoning Language Models

TL;DR

A compute-centric systematization of RL-style reasoning-model post-training, covering PPO and GRPO as well as intra-model, inter-model, synchronous, and asynchronous parallelism.

Why it matters

Reasoning-model training couples rollout actors, reward or reference models, and policy updates. That makes scaling a placement, scheduling, communication, and utilization problem in addition to an algorithmic one.

Key findings

1. 01

   The paper organizes reasoning-model training around the computational dependencies between generation, scoring, and policy optimization.

2. 02

   It extends familiar data, tensor, pipeline, sequence, context, and expert parallelism with multi-model strategies such as disaggregated placement and asynchronous execution.

3. 03

   The contribution is primarily a systems taxonomy and set of design guidelines rather than a single controlled scaling-law experiment.

Scaling dimensions

Models, methods & benchmarks

Algorithms

PPO, GRPO, Distributed training

Topics