Decompiling binary code into human-readable, high-level source code is a core challenge in reverse engineering. While traditional methods often rely on brittle, pattern-based heuristics, the advent of Large Language Models (LLMs) offers a more flexible and robust approach. However, current LLM-based decompilation efforts are often limited by their training methodologies, which typically treat the task as a simple sequence-to-sequence translation and struggle to enforce the functional correctness of the output. To address these issues, this paper proposes an innovative framework for training LLMs to perform high-fidelity decompilation. A core contribution of our work is a novel data processing pipeline that enriches the model’s input. This pipeline integrates Ghidra-based static analysis to directly embed crucial context, such as static resources (strings, floating-point numbers) and relabeled basic blocks—from the binary into an LLM-friendly prompt. Building on this enriched input, we employ reinforcement learning fine-tuning guided by a multi-faceted reward function that comprehensively evaluates syntactic correctness, AST similarity, compilability, and functional correctness via test cases. Using this framework, we trained the RlDecompiler family of models (1.3B and 3B). Experimental results demonstrate that RlDecompiler achieves state-of-the-art performance, and its generated code quality is also higher than that of the baseline models. The RlDecompiler 1.3B and 3B models achieve rerunnable rates of 27.96% and 40.70%, respectively, outperforming existing baselines. The code is available at https://anonymous.4open.science/r/rldecompile-19D0/.