SmartDecompiler-R1: Enhancing Faithful and Explainable Smart Contract Bytecode Decompilation with Reinforcement Learning

Abstract

Understanding EVM bytecode is critical for smart contract security analysis. Existing decompilers typically rely on heuristic rules or leverage large language models (LLMs) to generate source code after bytecode analysis. However, heuristic-based approaches often produce pseudocode that is difficult for humans to interpret, while LLM-based methods also face several problems. LLMs have little experience with low-level bytecode, which weakens their reasoning and causes inaccurate results. Additionally, their tendency to auto-correct code breaks faithfulness to the original program. A general lack of clarity in these approaches also hinders effective auditing and interpretation. In this paper, we propose SmartDecompiler-R1, an end-to-end decompilation framework that translates Three-Address Code (TAC), a register-based representation of EVM bytecode, into source code using reinforcement learning. SmartDecompiler-R1 significantly improves both the accuracy and consistency of decompiled code, while additionally providing human-readable explanations for the bytecode-to-source generation process. To the best of our knowledge, we are the first to design a benchmark equipped with well-defined test cases and a systematic evaluation framework for smart contract decompilation. Experimental results on this benchmark demonstrate that SmartDecompiler-R1 substantially outperforms existing decompilers on execution consistency by 46.23%. In particular, compared with LLM-based approaches, SmartDecompiler-R1 achieves a significant improvement in preserving vulnerability consistency between the original and decompiled contracts. Furthermore, we conduct extensive ablation studies to validate the effectiveness of individual components of SmartDecompiler-R1, and design targeted experiments to demonstrate the interpretability and readability of the generated explanations.