arXiv:2506.11027v3 Announce Type: replace Abstract: Generating accurate and executable code using Large Language Models (LLMs) remains a significant challenge for underrepresented programming languages, such as Prolog and Lisp, due to the scarcity of public training data compared to high-resource languages like Python. This paper introduces a generalizable Reinforcement Learning (RL) approach that combines small-scale versions of the Qwen2.5-Coder model with Group Relative Policy Optimization (GRPO) to enable effective code generation through reasoning. To address the limitations of sparse dat
The scarcity of training data for underrepresented languages has been a known bottleneck for LLM code generation, and this paper introduces a novel RL approach to address it. AI research continues to push boundaries in making LLMs more versatile.
Improving code generation for underrepresented languages expands the utility of LLMs, enabling more diverse development ecosystems and potentially reducing dependency on high-resource language stacks. This can democratize access to advanced AI programming tools beyond the dominant languages.
The ability to generate accurate code for languages like Prolog and Lisp with fewer training data requirements shifts the paradigm for LLM application, potentially making niche programming domains more accessible to automation. This could broaden the global developer pool and the types of problems AI can solve.
- · Developers in niche programming language communities
- · Organizations using underrepresented languages
- · AI model developers
- · Developing nations with diverse linguistic programming preferences
- · Monopolistic control of major AI ecosystems by high-resource languages
- · Companies whose competitive advantage relies solely on Python/JS-centric AI tool
LLMs become more proficient at generating code for a wider array of programming languages, even those with limited public data.
This could lead to a revitalization or increased adoption of currently underrepresented programming languages by making them easier to work with through AI assistance.
Reduced dependency on a few dominant programming languages could foster more diverse and resilient software ecosystems globally, potentially impacting digital sovereignty.
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Read at arXiv cs.LG