Researchers introduced Reinforcement Learning from Self-Feedback (RLSF), a post-training stage that refines large language models by leveraging their intrinsic confidence as a self-generated reward. This approach refines probability estimates and strengthens step-by-step reasoning, improving performance on arithmetic reasoning and multiple-choice question answering.