Transpilation, or code translation, aims to convert source code from one programming language (PL) to another. Recent large language model (LLM)-based approaches have demonstrated immense potential for code translation. Training-based methods are important because LLMs currently do not effectively adapt to domain-specific settings that suffer from a lack of knowledge without targeted training. However, existing training-based approaches rely on a pairwise transpilation paradigm, making it impractical to support a diverse range of PLs. Furthermore, these methods shape the reinforcement learning (RL) reward linearly in the test-case pass rate, which underweights the earliest partial progress. To address these limitations, we propose CodePivot, a training framework that leverages Python as an intermediate representation (IR), augmented by a novel RL reward mechanism, Aggressive-Partial-Functional reward, to bootstrap the model’s multilingual transpilation ability with reduced reliance on parallel corpora. Experiments involving 10 PLs show that the resulting 7B model, trained on Python-to-Others tasks, consistently improves performance across both general and low-resource PL-related transpilation tasks. It outperforms substantially larger mainstream models with hundreds of billions more parameters, such as DeepSeek-R1 and Qwen3-235B-A22B-Instruct-2507, on Python-to-Others tasks and Others-to-All tasks, respectively, and surpasses its counterpart trained directly on Any-to-Any tasks on general transpilation tasks. Code and data: https://github.com/lishangyu-hkust/CodePivot.