Google DeepMind has introduced a new method called SCoRe, which stands for Self-Correction via Reinforcement Learning, aimed at enhancing the self-correction abilities of large language models (LLMs). This multi-turn chain-of-thought online reinforcement learning approach uses entirely self-generated data to improve the accuracy of LLMs in complex mathematical and coding tasks. SCoRe has demonstrated significant improvements, including a 15.6% gain in self-correction for reasoning problems from the MATH dataset and a 9.1% improvement in HumanEval. Additionally, SCoRe combined with inference-time scaling (maj@32) achieves a 10.5% improvement. The method represents a significant advancement in AI, addressing the challenge of self-correction without external guidance.