# Static Neural Compiler Optimization via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2008.08951 · HTML: https://mlsanity.com/paper/2008.08951/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 14 of 14)

- [CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research](https://mlsanity.com/paper/2109.08267/) (2021)
- [Towards Scalable Verification of Deep Reinforcement Learning](https://mlsanity.com/paper/2105.11931/) (2021)
- [Learning Compiler Pass Orders using Coreset and Normalized Value Prediction](https://mlsanity.com/paper/2301.05104/) (2023)
- [Verifying Generalization in Deep Learning](https://mlsanity.com/paper/2302.05745/) (2023)
- [CompilerDream: Learning a Compiler World Model for General Code Optimization](https://mlsanity.com/paper/2404.16077/) (2024)
- [A Reinforcement Learning Environment for Polyhedral Optimizations](https://mlsanity.com/paper/2104.13732/) (2021)
- [RL4ReAl: Reinforcement Learning for Register Allocation](https://mlsanity.com/paper/2204.02013/) (2022)
- [POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning](https://mlsanity.com/paper/2208.04238/) (2022)
- [Power Constrained Autotuning using Graph Neural Networks](https://mlsanity.com/paper/2302.11467/) (2023)
- [Target-independent XLA optimization using Reinforcement Learning](https://mlsanity.com/paper/2308.14364/) (2023)
- [Protean Compiler: An Agile Framework to Drive Fine-grain Phase Ordering](https://mlsanity.com/paper/2602.06142/) (2026)
- [ReLATE: Accelerating Tensor Decomposition via Safe and Efficient Learning of Sparse Encodings](https://mlsanity.com/paper/2509.00280/) (2025)
- [The New Compiler Stack: A Survey on the Synergy of LLMs and Compilers](https://mlsanity.com/paper/2601.02045/) (2026)
- [CompileRover: Revolutionizing Virtual Machine Compiler Optimization with a Tri-Role LLM-Driven Framework](https://mlsanity.com/paper/2609.19004/) (2026)

## Abstract (arXiv)

The phase-ordering problem of modern compilers has received a lot of attention from the research community over the years, yet remains largely unsolved. Various optimization sequences exposed to the user are manually designed by compiler developers. In designing such a sequence developers have to choose the set of optimization passes, their parameters and ordering within a sequence. Resulting sequences usually fall short of achieving optimal runtime for a given source code and may sometimes even degrade the performance when compared to unoptimized version. In this paper, we employ a deep reinforcement learning approach to the phase-ordering problem. Provided with sub-sequences constituting LLVM's O3 sequence, our agent learns to outperform the O3 sequence on the set of source codes used for training and achieves competitive performance on the validation set, gaining up to 1.32x speedup on previously-unseen programs. Notably, our approach differs from autotuning methods by not depending on one or more test runs of the program for making successful optimization decisions. It has no dependence on any dynamic feature, but only on the statically-attainable intermediate representation of the source code. We believe that the models trained using our approach can be integrated into modern compilers as neural optimization agents, at first to complement, and eventually replace the hand-crafted optimization sequences.