ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation

Code generation plays a crucial role in various tasks, such as code auto-completion and mathematical reasoning.Previous work has proposed numerous methods to enhance code generation performance, including integrating feedback from the compiler.Inspired by this, we present ReflectionCoder, a novel approach that effectively leverages reflection sequences constructed by integrating compiler feedback to improve one-off code generation performance.Furthermore, we propose reflection self-distillation and dynamically masked distillation to effectively utilize these reflection sequences.Extensive experiments on three benchmarks, i.e., HumanEval (+), MBPP (+), and MultiPL-E, demonstrate that models fine-tuned with our method achieve state-of-the-art performance.Beyond the code domain, we believe this approach can benefit other domains that focus on final results and require long reasoning paths.