Self-Execution Simulation Improves Coding Models
arXiv:2604.03253v1 Announce Type: new Abstract: A promising research direction in enabling LLMs to generate consistently correct code involves addressing their inability to properly estimate program execution, particularly for code they generate. In this work, we demonstrate that Code LLMs can be trained to simulate program execution in a step-by-step manner and that this capability can be leveraged to improve competitive programming performance. Our approach combines supervised fine-tuning on natural language execution traces, textual explanations grounded in true execution, with reinforcement learning using verifiable rewards. We introduce two complementary objectives: output prediction given code and inputs, and solving competitive programming tasks with either ground-truth or self-predicted execution feedback. These objectives enable models to perform self-verification over multiple candidate solutions, and iterative self-fixing by simulating test execution. Across multiple compet
arXiv:2604.03253v1 Announce Type: new Abstract: A promising research direction in enabling LLMs to generate consistently correct code involves addressing their inability to properly estimate program execution, particularly for code they generate. In this work, we demonstrate that Code LLMs can be trained to simulate program execution in a step-by-step manner and that this capability can be leveraged to improve competitive programming performance. Our approach combines supervised fine-tuning on natural language execution traces, textual explanations grounded in true execution, with reinforcement learning using verifiable rewards. We introduce two complementary objectives: output prediction given code and inputs, and solving competitive programming tasks with either ground-truth or self-predicted execution feedback. These objectives enable models to perform self-verification over multiple candidate solutions, and iterative self-fixing by simulating test execution. Across multiple competitive programming benchmarks, our method yields consistent improvements over standard reasoning approaches. We further present ablations and analysis to elucidate the role of execution simulation and its limitations.
Executive Summary
This article presents a novel approach to improving the performance of Large Language Models (LLMs) in code generation by enabling them to simulate program execution. The authors propose a two-objective training method that combines supervised fine-tuning and reinforcement learning to empower LLMs with the ability to predict program execution and self-verify their generated code. The approach is tested on competitive programming benchmarks, yielding consistent improvements over standard reasoning approaches. The study offers insights into the role of execution simulation in code generation and its limitations, paving the way for further research and applications in the field of artificial intelligence and coding.
Key Points
- ▸ The article presents a self-execution simulation approach to improve LLMs in code generation.
- ▸ The method combines supervised fine-tuning and reinforcement learning for execution simulation.
- ▸ The approach is tested on competitive programming benchmarks, demonstrating improved performance.
Merits
Improved Code Generation
The self-execution simulation approach enables LLMs to generate more accurate and reliable code, reducing the likelihood of errors and increasing overall performance.
Enhanced Verification
The method empowers LLMs with the ability to self-verify their generated code, ensuring that it meets the required standards and specifications.
Demerits
Complexity and Computational Cost
The proposed approach requires significant computational resources and may increase the complexity of the training process, potentially limiting its adoption in resource-constrained environments.
Limited Generalizability
The study focuses on competitive programming benchmarks, and its findings may not be directly applicable to other domains or programming tasks, requiring further research and adaptation.
Expert Commentary
While the study presents a promising approach to improving LLMs in code generation, its limitations and potential biases must be carefully considered. The reliance on competitive programming benchmarks may limit the generalizability of the findings, and the increased complexity and computational cost of the proposed method may hinder its adoption in certain environments. Nevertheless, the work contributes significantly to the ongoing research in code generation and verification, and its implications for the development of more reliable and secure coding frameworks are substantial. As the field continues to evolve, it is essential to explore the potential applications and limitations of execution simulation in code generation, ensuring that these technologies are developed and deployed responsibly.
Recommendations
- ✓ Further research is needed to explore the generalizability of the proposed approach to various coding tasks and domains.
- ✓ The development of more efficient and scalable algorithms for execution simulation is essential to reducing the computational cost and increasing the practicality of the method.
Sources
Original: arXiv - cs.CL