Breaking Training Bottlenecks: Effective and Stable Reinforcement Learning for Coding Models

Modern code generation models exhibit longer outputs, accelerated capability growth, and changed training dynamics, rendering traditional training methodologies, algorithms, and datasets ineffective for improving their performance. To address these training bottlenecks, we propose MicroCoder-GRPO, an improved Group Relative Policy Optimization approach with two original innovations and one inherited improvement: conditional truncation masking to improve long output potential while maintaining training stability, diversity-determined temperature selection to maintain and encourage output diversity, and removal of KL loss with high clipping ratios following DAPO to facilitate solution diversity. MicroCoder-GRPO achieves up to 17.6% relative improvement over strong baselines on LiveCodeBench v6, with more pronounced gains under extended context evaluation. Additionally, we release MicroCoder-Dataset, a more challenging training corpus that achieves 3x larger performance gains than mainstream datasets on LiveCodeBench v6 within 300 training steps, and MicroCoder-Evaluator, a robust framework that reduces disagreement with human labels from 10.8% to 1.4% and around 40% faster execution through parallel processing. Through comprehensive analysis across more than thirty controlled experiments, we reveal training insights across seven main aspects, demonstrating that properly trained models can achieve competitive performance with larger counterparts.