# MLGO: a Machine Learning Guided Compiler Optimizations Framework

arXiv: https://arxiv.org/abs/2101.04808 · HTML: https://mlsanity.com/paper/2101.04808/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 13 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [Learning to Optimize Tensor Programs](https://mlsanity.com/paper/1805.08166/) (2018)
- [Structured Evolution with Compact Architectures for Scalable Policy Optimization](https://mlsanity.com/paper/1804.02395/) (2018)
- [NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning](https://mlsanity.com/paper/1909.13639/) (2019)

## Built on by (most cited first, 30 of 40)

- [Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress](https://mlsanity.com/paper/2206.01626/) (2022)
- [Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs](https://mlsanity.com/paper/2407.00945/) (2024)
- [Large Language Models for Compiler Optimization](https://mlsanity.com/paper/2309.07062/) (2023)
- [CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research](https://mlsanity.com/paper/2109.08267/) (2021)
- [Meta Large Language Model Compiler: Foundation Models of Compiler Optimization](https://mlsanity.com/paper/2407.02524/) (2024)
- [STARK: Strategic Team of Agents for Refining Kernels](https://mlsanity.com/paper/2510.16996/) (2025)
- [Compiler Optimization for Quantum Computing Using Reinforcement Learning](https://mlsanity.com/paper/2212.04508/) (2022)
- [Learning Compiler Pass Orders using Coreset and Normalized Value Prediction](https://mlsanity.com/paper/2301.05104/) (2023)
- [Regularized Langevin Dynamics for Combinatorial Optimization](https://mlsanity.com/paper/2502.00277/) (2025)
- [MLGOPerf: An ML Guided Inliner to Optimize Performance](https://mlsanity.com/paper/2207.08389/) (2022)
- [GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation](https://mlsanity.com/paper/2210.03894/) (2022)
- [Compiler generated feedback for Large Language Models](https://mlsanity.com/paper/2403.14714/) (2024)
- [SORREL: Suboptimal-Demonstration-Guided Reinforcement Learning for Learning to Branch](https://mlsanity.com/paper/2412.15534/) (2024)
- [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)
- [Learning to Combine Instructions in LLVM Compiler](https://mlsanity.com/paper/2202.12379/) (2022)
- [RL4ReAl: Reinforcement Learning for Register Allocation](https://mlsanity.com/paper/2204.02013/) (2022)
- [Generating GPU Compiler Heuristics using Reinforcement Learning](https://mlsanity.com/paper/2111.12055/) (2021)
- [Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization](https://mlsanity.com/paper/2403.19462/) (2024)
- [REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Serving](https://mlsanity.com/paper/2506.01374/) (2025)
- [Agentic Harness for Real-World Compilers](https://mlsanity.com/paper/2603.20075/) (2026)
- [Profile Guided Optimization without Profiles: A Machine Learning Approach](https://mlsanity.com/paper/2112.14679/) (2021)
- [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)
- [LoopTune: Optimizing Tensor Computations with Reinforcement Learning](https://mlsanity.com/paper/2309.01825/) (2023)
- [Towards LLM-based optimization compilers. Can LLMs learn how to apply a single peephole optimization? Reasoning is all LLMs need!](https://mlsanity.com/paper/2412.12163/) (2024)
- [VeriLocc: End-to-End Cross-Architecture Register Allocation via LLM](https://mlsanity.com/paper/2506.17506/) (2025)
- [Protean Compiler: An Agile Framework to Drive Fine-grain Phase Ordering](https://mlsanity.com/paper/2602.06142/) (2026)
- [AI-PROPELLER: Warehouse-Scale Interprocedural Code Layout Optimization with AlphaEvolve](https://mlsanity.com/paper/2606.00131/) (2026)
- [AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning](https://mlsanity.com/paper/2606.20373/) (2026)

## Abstract (arXiv)

Leveraging machine-learning (ML) techniques for compiler optimizations has been widely studied and explored in academia. However, the adoption of ML in general-purpose, industry strength compilers has yet to happen. We propose MLGO, a framework for integrating ML techniques systematically in an industrial compiler -- LLVM. As a case study, we present the details and results of replacing the heuristics-based inlining-for-size optimization in LLVM with machine learned models. To the best of our knowledge, this work is the first full integration of ML in a complex compiler pass in a real-world setting. It is available in the main LLVM repository. We use two different ML algorithms: Policy Gradient and Evolution Strategies, to train the inlining-for-size model, and achieve up to 7% size reduction, when compared to state of the art LLVM -Oz. The same model, trained on one corpus, generalizes well to a diversity of real-world targets, as well as to the same set of targets after months of active development. This property of the trained models is beneficial to deploy ML techniques in real-world settings.