Large language models (LLMs) excel at reasoning, yet post-training remains critical for aligning their behavior with task goals. Existing reinforcement learning (RL) methods often depend on costly human annotations or external reward models. We propose Reinforcement Learning via Self-Confidence (RLSC), which uses the model's own confidence as reward signals-eliminating the need for labels, preference models, or reward engineering. Applied to Qwen2.5-Math-7B with only 8 samples per question and 4 training epochs, RLSC improves accuracy by +20.10% on AIME2024, +49.40% on MATH500, and +52.50% on AMC23. RLSC offers a simple, scalable post-training method for reasoning models with minimal supervision.