# Branch Prediction as a Reinforcement Learning Problem: Why, How and Case Studies

arXiv: https://arxiv.org/abs/2106.13429 · HTML: https://mlsanity.com/paper/2106.13429/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 25 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)

## Built on by (most cited first, 3 of 3)

- [Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning](https://mlsanity.com/paper/2109.12021/) (2021)
- [A Survey of Deep Learning Techniques for Dynamic Branch Prediction](https://mlsanity.com/paper/2112.14911/) (2021)
- [Athena: Synergizing Data Prefetching and Off-Chip Prediction via Online Reinforcement Learning](https://mlsanity.com/paper/2601.17615/) (2026)

## Abstract (arXiv)

Recent years have seen stagnating improvements to branch predictor (BP) efficacy and a dearth of fresh ideas in branch predictor design, calling for fresh thinking in this area. This paper argues that looking at BP from the viewpoint of Reinforcement Learning (RL) facilitates systematic reasoning about, and exploration of, BP designs. We describe how to apply the RL formulation to branch predictors, show that existing predictors can be succinctly expressed in this formulation, and study two RL-based variants of conventional BPs.