Through reinforcement learning (RL) with outcome correctness rewards, large reasoning models (LRMs) have demonstrated substantial success on complex reasoning tasks, leveraging scaled inference computation. However, the sparse and one-sided reward, focused solely on final correctness, limit its ability to provide detailed supervision over internal reasoning process. This deficiency leads to suboptimal reasoning quality, manifesting as issues like over-thinking, under-thinking, redundant-thinking, and disordered-thinking. Inspired by the recent progress in LRM self-rewarding, we introduce a self-rewriting framework, where a model rewrites its own reasoning texts, and subsequently learns from the rewritten reasoning to improve the internal thought process quality. For algorithm design, we propose a selective rewriting approach wherein only "simple" samples, defined by the model's consistent correctness, are rewritten, thereby preserving all original loss of GRPO. For practical implementation, we compile rewriting and vanilla generating within one single batch, maintaining the scalability of the RL algorithm and introducing only 10\% overhead. Extensive experiments on diverse tasks with different model sizes validate the effectiveness of self-rewriting. In terms of the accuracy-length tradeoff, the self-rewriting approach achieves improved accuracy (+0.6) with substantially shorter reasoning (-46\%) even without explicit instructions to truncate reasoning, outperforming exsiting strong baselines. In terms of internal quality, self-rewriting achieves significantly higher scores (+7.2) under the LLM-as-a-judge metric. All relevant code and data will be released.