mlsanityData to 7 Oct 2026

RL4ReAl: Reinforcement Learning for Register Allocation

arXiv 2204.02013 ↗First posted 5 Apr 2022cs.LG cs.AR cs.PLcited by 4

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Abstract (arXiv)

We aim to automate decades of research and experience in register allocation, leveraging machine learning. We tackle this problem by embedding a multi-agent reinforcement learning algorithm within LLVM, training it with the state of the art techniques. We formalize the constraints that precisely define the problem for a given instruction-set architecture, while ensuring that the generated code preserves semantic correctness. We also develop a gRPC based framework providing a modular and efficient compiler interface for training and inference. Our approach is architecture independent: we show experimental results targeting Intel x86 and ARM AArch64. Our results match or out-perform the heavily tuned, production-grade register allocators of LLVM.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 18 of its 74 references that are in the map. First posted on arXiv 5 Apr 2022.

Builds on

18 of 74 references.

References · 18

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
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  4. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 730
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  10. 2021CompilerGym: Robust, Performant Compiler Optimization Environments for AI Researchcited by 41
  11. 2019Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problemscited by 40
  12. 2021MLGO: a Machine Learning Guided Compiler Optimizations Frameworkcited by 40
6 more
  1. 2019NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learningcited by 37
  2. 2020Static Neural Compiler Optimization via Deep Reinforcement Learningcited by 14
  3. 2019Coloring Big Graphs with AlphaGoZerocited by 13
  4. 2019AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learningcited by 12
  5. 2018Combinatorial Register Allocation and Instruction Schedulingcited by 7
  6. 2020Optimizing Memory Placement using Evolutionary Graph Reinforcement Learningcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 2.

4 papers.

  1. 2026Agentic Harness for Real-World Compilerscited by 3
  2. 2025VeriLocc: End-to-End Cross-Architecture Register Allocation via LLMcited by 1
  3. 2026CHEHAB RL: Learning to Optimize Fully Homomorphic Encryption Computationscited by 1
  4. 2023The Next 700 ML-Enabled Compiler Optimizationscited by 0