Selective Left-Shift: Turning Test-Time Compute and Difficulty-based Curation into Training Data for Low-Resource Code Generation
arXiv:2607.07748v1 Announce Type: new Abstract: Large Language Models achieve strong code generation for high resource languages like Python and Java but suffer sharp performance drops on Low-Resource Programming Languages~(LRPLs) such as Julia. Improving Small Language Models~(SLMs) for these languages faces a trilemma: Supervised Fine-Tuning~(SFT) is bottlenecked by data scarcity, inference-time scaling is too expensive for deployment, and Reinforcement Learning from scratch yields near zero advantages. We propose a three-phase pipeline that resolves this trilemma by decoupling syntax acquis
The proliferation of Large Language Models (LLMs) has revealed a significant performance gap in code generation for low-resource programming languages, prompting the need for more efficient training solutions for Small Language Models (SLMs).
This development could democratize advanced AI code generation capabilities by making them viable for a broader range of programming languages and potentially less computationally intensive, thus enabling wider adoption and innovation.
The proposed 'Selective Left-Shift' pipeline offers a method to overcome data scarcity and computational costs for training SLMs in low-resource programming languages, potentially accelerating their development and deployment.
- · Developers in low-resource programming languages (e.g., Julia)
- · Small Language Model (SLM) developers
- · Organizations seeking efficient, specialized code generation tools
- · AI research focused on efficiency and resource optimization
- · Developers solely relying on general-purpose LLMs for specialized tasks
- · High-resource programming language ecosystems that become relatively less unique
More efficient and accurate code generation for low-resource programming languages will become accessible.
This improved accessibility could lead to a revitalization or increased adoption of these languages in specialized domains, reducing dependency on a few dominant ones.
Reduced compute requirements for specialized code generation models could lower the barrier to entry for AI development, fostering a more diverse and globally distributed AI innovation landscape.
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Read at arXiv cs.LG