This paper introduces Semantic Soft Bootstrapping (SSB), a novel self-distillation technique for enhancing long-context reasoning in large language models without relying on reinforcement learning. SSB uses the same base LLM as both teacher and student, leveraging semantically rich contexts to generate robust explanations and distilling logit-level supervision to improve reasoning capabilities efficiently.