Requirement-Aware Curriculum Reinforcement Learning: Teaching LLMs to Code Step by Step
> Paper: Improving LLM Code Generation via Requirement-Aware Curriculum Reinforcement Learning > Authors: Shouyu Yin, Zhao Tian, Junjie Chen, Shikai Guo > arXiv: 2605.00433 | 2026-04-29
The Problem: LLMs Jump Straight into Complex Code
Imagine teaching a student to program: on day one you ask for "Hello World" and they hand you a distributed microservice architecture. The code may look advanced, but the fundamentals are shaky—simple problems get over-engineered, and complex problems overwhelm them.
This mirrors today's LLM code generation: models produce complete code directly, with no "simple-to-complex" learning process, limiting performance on complex requirements.
The Wisdom of Curriculum Learning
Humans learn programming in stages: variables and types, conditionals, loops, functions, object orientation, design patterns, then system architecture—each step building on the last. LLM training, by contrast, is like building a tower from the ground floor: it sees simple and complex code examples together with no explicit difficulty progression, leading to over-complication on easy tasks and weak decomposition on hard ones.
Requirement-Aware Curriculum RL
The paper proposes Requirement-Aware Curriculum Reinforcement Learning: automatically design a curriculum based on the complexity of the programming requirement, so the LLM learns code generation progressively.
Technical approach:
1. Requirement complexity assessment — analyze natural-language requirements considering the number of feature points, data structure complexity, algorithmic difficulty, and code length. 2. Automatic curriculum generation — start from simple requirements and gradually increase complexity: single functions first, then multi-function compositions, then complete modules. 3. Reinforcement learning optimization — train the LLM on curriculum tasks with rewards for code correctness, requirement matching, and code conciseness. 4. Requirement awareness — the curriculum is not merely "easy to hard"; curriculum tasks are structurally similar to the target requirement, so every step contributes to the final capability.
It works like a wise teacher designing progressive exercises aligned with the exam goal.
Why Curriculum RL Beats Direct Training
Direct training mixes simple and complex code so the model cannot distinguish difficulty, and rewards for complex tasks are sparse. Curriculum RL instead:
- Builds foundational capability from dense rewards on simple tasks
- Improves steadily, each step building on the previous one
- Keeps curriculum design aligned with the final goal, avoiding irrelevant skills
A Feynman-Style Takeaway
> "Good code generation models should write both simple and complex code—but complex ability is built on a simple foundation. Curriculum RL lets models master programming progressively, like humans do."
Key Questions for Practitioners
1. Does my training data have an explicit difficulty progression? 2. Does the model start from simple tasks and work toward complex ones? 3. Is the curriculum design aligned with the final objective?
Core insight: code generation is not just about "writing it right" but about "learning to write." When LLMs learn programming step by step like students, they generate better code and gain scalable programming ability. On the road to software engineering automation, curriculum learning is the bridge to complexity.