# RL in the Wild: Characterizing RLVR Training in LLM Deployment

arXiv: https://arxiv.org/abs/2509.25279 · HTML: https://mlsanity.com/paper/2509.25279/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2025.

## Built on by (most cited first, 2 of 2)

- [Role-Based Fault Tolerance System for LLM RL Post-Training](https://mlsanity.com/paper/2512.22492/) (2025)
- [Belayer: Efficient Fault Tolerance for LLM Agentic RL Training](https://mlsanity.com/paper/2608.14635/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are now widely used across many domains. With their rapid development, Reinforcement Learning with Verifiable Rewards (RLVR) has surged in recent months to enhance their reasoning and understanding abilities. However, its complex data flows and diverse tasks pose substantial challenges to RL training systems, and there is limited understanding of RLVR from a system perspective. To thoroughly understand the system challenges introduced by RLVR, we present a characterization study of RLVR tasks in our LLM deployment. Specifically, we investigate the distribution and variation trends of workloads across different RL tasks across training steps. We identify issues such as GPU idling caused by skewed sequence length distribution, inefficient parallel strategies in dynamically varying workloads, inefficient data management mechanisms, and load imbalance. We describe our observations and call for further investigation into the remaining open challenges. Furthermore, we propose PolyTrace benchmark suite to conduct evaluation with realistic workloads, and a practical use case validates that PolyTrace benchmark suite exhibits 94.7% accuracy.