CompilerDream: MBRL for Code Optimization

• CompilerDream is a reinforcement learning framework that employs a simulated compiler world model and an optimization agent to determine effective pass sequences.
• Its model-based methodology leverages large-scale training data to achieve rapid policy improvements and robust zero-shot generalization across diverse languages.
• Benchmarking demonstrates that CompilerDream outperforms static heuristics and current learning-based optimizers in value prediction and end-to-end code optimization tasks.

CompilerDream is a model-based reinforcement learning framework for general code optimization within compiler design. Traditional compiler approaches often utilize static, fixed sequences of optimization passes. In contrast, CompilerDream addresses the challenges inherent in selecting and ordering optimization passes with a world model that simulates the effect of these passes and an agent that learns to construct optimal strategies across varied datasets and programming languages. The framework demonstrates robust zero-shot generalization and surpasses legacy compiler heuristics and other state-of-the-art learning-based optimizers in both value prediction and end-to-end code optimization tasks (Deng et al., 2024).

1. Foundations and Motivation

Code optimization is central to compiler design and impacts both runtime efficiency and resource utilization in software engineering. Conventionally, compilers perform optimization through a predetermined sequence of passes—transformations applied to source code or intermediate representation to improve performance or reduce size. The efficacy of these passes depends not only on their selection but critically on their ordering, given complex interdependencies among transformations. Existing methods for discovering optimal pass sequences are hindered by either slow combinatorial search or learning-based models with poor generalization to unseen code (Deng et al., 2024). CompilerDream emerges from the requirement for efficient, general-purpose code optimization across diverse domains.

2. Framework Architecture

CompilerDream consists of two principal components:

• Compiler World Model: Simulates the effects of optimization passes on program code, capturing intrinsic properties of transformations.
• Optimization Agent: Trained on the world model, the agent learns strategies for selecting and ordering passes to optimize code efficiently.

Training utilizes a large-scale dataset of programs, enabling the model to generalize optimization strategies across multiple languages and application scenarios.

Component	Description	Role in Workflow
Compiler World Model	Simulates behavior and effects of passes on code	Environment for agent training
Optimization Agent	Learns sequence policies via reinforcement learning	Selects and orders optimization

3. Model-Based Reinforcement Learning Methodology

A defining characteristic of CompilerDream is its model-based reinforcement learning (MBRL) approach. Rather than optimizing code directly on real compilers or benchmarks, the agent interacts with the compiled world model, enabling rapid simulation of optimization decisions and their outcomes. This alleviates the inefficiencies of real-environment search and expands the diversity of training scenarios. The MBRL paradigm allows for iterative policy improvement and learning from both synthetic and real datasets, which is integral to the framework’s capacity for zero-shot generalization (Deng et al., 2024).

4. Generalization and Large-Scale Training

CompilerDream is trained on large and heterogeneous datasets of programs. This breadth enables the world model and agent to learn generalized representations of code structure and optimization effects independent of specific programming languages or codebases. The empirical results demonstrate strong zero-shot generalization: the trained model and agent excel on datasets not observed during training, outperforming both LLVM’s built-in optimizations and competing learning-based methods in value prediction and end-to-end code optimization tasks. This suggests CompilerDream’s applicability as a generic code optimization engine (Deng et al., 2024).

5. Experimental Evaluation and Benchmarking

Extensive experiments position CompilerDream at the forefront of compiler autotuning, as measured on the CompilerGym leaderboard. Benchmarking against LLVM’s default pipeline and other state-of-the-art methods confirms measurable performance gains in both optimization quality and generalization. The results underscore the value of modeling compiler transformations using learned dynamics, validating the efficacy of the MBRL methodology and the utility of large-scale training.

6. Comparative Context and Implications

Most compilers employ static, non-adaptive optimization pipelines, while recent learning-based methods struggle to generalize and scale. CompilerDream distinguishes itself via its world model architecture and large-scale training regime. The success across varied datasets and languages signals a plausible implication for future compiler design, suggesting that learned optimization strategies, trained on simulated compiler environments, may supersede static heuristics and hand-crafted pipelines in both research and production contexts (Deng et al., 2024).

7. Future Directions and Research Opportunities

The development of CompilerDream initiates several research vectors, including refinement of compiler world models, investigation of transfer learning between programming languages, and integration into existing compiler toolchains. Further, its demonstrated zero-shot generalization invites exploration into emergent representation learning and dynamic policy adaptation for unseen codebases. A plausible implication is that continued advancement of model-based approaches may redefine standards for general-purpose code optimization.