LeGo-Code: Can Modular Curriculum Learning Advance Complex Code Generation? Insights from Text-to-SQL

2026-04-20 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, quick

Summary

LeGo-Code introduces a Modular Adapter Composition (MAC) strategy to enhance large language models' (LLMs) performance in complex Text-to-SQL tasks, addressing challenges with deeply nested statements and noisy database schemas. Traditional fine-tuning and naive curriculum learning, which simply orders training samples by complexity, often fail due to catastrophic forgetting. The MAC strategy involves sequentially training tier-specific adapters on incremental complexity levels, from Easy to Extra-Hard, creating a scaffolded learning environment. This approach yielded measurable performance gains on the Spider and BIRD benchmarks. The resulting "Lego-like" architecture offers flexibility, allowing models to be composed and deployed based on specific schema difficulty requirements, demonstrating that structured, modular learning is more effective than monolithic fine-tuning for complex code generation.

Key takeaway

For AI Engineers developing LLMs for Text-to-SQL, consider implementing a modular adapter composition strategy. This approach, which trains adapters on incremental complexity levels, can significantly improve performance on complex queries and noisy schemas, offering a flexible architecture for deployment based on specific database difficulty. Avoid monolithic fine-tuning for such tasks, as it risks catastrophic forgetting.

Key insights

Modular curriculum learning with tier-specific adapters improves LLM performance on complex Text-to-SQL tasks.

Principles

• Modular learning prevents catastrophic forgetting.
• Sequential training on complexity tiers enhances performance.

Method

The Modular Adapter Composition (MAC) strategy sequentially trains tier-specific adapters on incrementally complex data, from Easy to Extra-Hard, to build a scaffolded learning environment.

In practice

• Use tier-specific adapters for complex code generation.
• Apply scaffolded learning to improve LLM accuracy.

Topics

• Complex Code Generation
• Text-to-SQL
• Curriculum Learning
• Modular Adapter Composition
• Large Language Models

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.