BatCoder: Self-Supervised Bidirectional Code-Documentation Learning via Back-Translation
Training LLMs for code-related tasks typically depends on high-quality code-documentation pairs, which are costly to curate and often scarce for niche programming languages. We introduce BatCoder, a self-supervised reinforcement learning framework designed to jointly optimize code generation and documentation production. BatCoder employs a back-translation strategy: a documentation is first generated from code, and then the generated documentation is used to reconstruct the original code. The semantic similarity between the original and reconstructed code serves as an implicit reward, enabling reinforcement learning to improve the model's performance both in generating code from documentation and vice versa. This approach allows models to be trained using only code, substantially increasing the available training examples. Evaluated on HumanEval and MBPP with a 7B model, BatCoder achieved 83.5% and 81.0% pass@1, outperforming strong open-source baselines. Moreover, the framework demonstrates consistent scaling with respect to both training corpus size and model capacity.
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Jingwen Xu
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Yiyang Lu
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Zisu Huang
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2026-02-09T14:31:43.838Z
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Zisu Huang
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Changze Lv
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2026-02-05T10:55:18.660Z
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Changze Lv
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Xiaohua Wang
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Shizheng Li
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Zhibo Xu
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Zhengkang Guo
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Zhengyuan Wang
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Muzhao Tian
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Xuanjing Huang
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Xiaoqing Zheng
BatCoder: Self-Supervised Bidirectional Code-Documentation Learning via Back-Translation
Training LLMs for code-related tasks typically depends on high-quality code-documentation pairs, which are costly to curate and often scarce for niche programming languages. We introduce BatCoder, a self-supervised reinforcement learning framework designed to jointly optimize code generation and documentation production. BatCoder employs a back-translation strategy: a documentation is first generated from code, and then the generated documentation is used to reconstruct the original code. The semantic similarity between the original and reconstructed code serves as an implicit reward, enabling reinforcement learning to improve the model's performance both in generating code from documentation and vice versa. This approach allows models to be trained using only code, substantially increasing the available training examples. Evaluated on HumanEval and MBPP with a 7B model, BatCoder achieved 83.5% and 81.0% pass@1, outperforming strong open-source baselines. Moreover, the framework demonstrates consistent scaling with respect to both training corpus size and model capacity.
BatCoder is a self-supervised reinforcement learning framework that jointly optimizes code and documentation generation through back-translation, achieving superior performance on code-related benchmarks.
self-supervised reinforcement learning back-translation code generation documentation production semantic similarity implicit reward reinforcement learning pass@1 model capacity