# Reinforcement Learning for Dynamic Workflow Optimization in CI/CD Pipelines

arXiv: https://arxiv.org/abs/2601.11647 · HTML: https://mlsanity.com/paper/2601.11647/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 4 of 4)

- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Safe Exploration in Continuous Action Spaces](https://mlsanity.com/paper/1801.08757/) (2018)
- [Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration](https://mlsanity.com/paper/1811.04122/) (2018)
- [Detecting Continuous Integration Skip : A Reinforcement Learning-based Approach](https://mlsanity.com/paper/2405.09657/) (2024)

## Abstract (arXiv)

Continuous Integration and Continuous Deployment (CI/CD) pipelines are central to modern software delivery, yet their static workflows often introduce inefficiencies as systems scale. This paper proposes a reinforcement learning (RL) based approach to dynamically optimize CI/CD pipeline workflows. The pipeline is modeled as a Markov Decision Process, and an RL agent is trained to make runtime decisions such as selecting full, partial, or no test execution in order to maximize throughput while minimizing testing overhead. A configurable CI/CD simulation environment is developed to evaluate the approach across build, test, and deploy stages. Experimental results show that the RL optimized pipeline achieves up to a 30 percent improvement in throughput and approximately a 25 percent reduction in test execution time compared to static baselines, while maintaining a defect miss rate below 5 percent. The agent learns to selectively skip or abbreviate tests for low risk commits, accelerating feedback cycles without significantly increasing failure risk. These results demonstrate the potential of reinforcement learning to enable adaptive and intelligent DevOps workflows, providing a practical pathway toward more efficient, resilient, and sustainable CI/CD automation.