StepCoder is a novel reinforcement learning (RL) framework for code generation that addresses challenges in exploring the output space and optimizing code generation quality. The framework consists of two main components: Curriculum of Code Completion Subtasks (CCCS) and Fine-Grained Optimization (FGO). CCCS breaks down complex code generation tasks into a sequence of easier sub-tasks, making exploration more manageable. FGO focuses on optimizing only the executed code segments, improving the precision of the optimization process. Additionally, the authors introduce the APPS+ dataset, a high-quality dataset manually verified to ensure the correctness of unit tests, which is used for training the RL model. The paper discusses the challenges of using RL in code generation, including the difficulty of exploring long sequences of code and the inefficiency of optimizing code based on unexecuted code snippets. The authors propose StepCoder to overcome these challenges by simplifying the exploration process and focusing on executed code. Experimental results show that StepCoder outperforms state-of-the-art approaches in code generation benchmarks, demonstrating its effectiveness in improving code generation quality. The paper also evaluates the performance of StepCoder on widely used benchmarks such as MBPP and HumanEval, showing that it achieves higher accuracy and efficiency compared to other methods. The authors further analyze the results of unit tests, finding that StepCoder is less prone to compilation errors but still faces challenges with runtime errors. The study highlights the importance of using compiler feedback to improve code generation quality and the potential of RL in enhancing code generation through precise optimization.StepCoder is a novel reinforcement learning (RL) framework for code generation that addresses challenges in exploring the output space and optimizing code generation quality. The framework consists of two main components: Curriculum of Code Completion Subtasks (CCCS) and Fine-Grained Optimization (FGO). CCCS breaks down complex code generation tasks into a sequence of easier sub-tasks, making exploration more manageable. FGO focuses on optimizing only the executed code segments, improving the precision of the optimization process. Additionally, the authors introduce the APPS+ dataset, a high-quality dataset manually verified to ensure the correctness of unit tests, which is used for training the RL model. The paper discusses the challenges of using RL in code generation, including the difficulty of exploring long sequences of code and the inefficiency of optimizing code based on unexecuted code snippets. The authors propose StepCoder to overcome these challenges by simplifying the exploration process and focusing on executed code. Experimental results show that StepCoder outperforms state-of-the-art approaches in code generation benchmarks, demonstrating its effectiveness in improving code generation quality. The paper also evaluates the performance of StepCoder on widely used benchmarks such as MBPP and HumanEval, showing that it achieves higher accuracy and efficiency compared to other methods. The authors further analyze the results of unit tests, finding that StepCoder is less prone to compilation errors but still faces challenges with runtime errors. The study highlights the importance of using compiler feedback to improve code generation quality and the potential of RL in enhancing code generation through precise optimization.