Reasoning is fundamental to human intelligence and critical for problem-solving. In practice, answering complex questions often requires abundant reasoning steps involving long reasoning paths. Existing multi-hop reasoning methods mainly focus on short-hop questions with only short reasoning paths, whose distribution is different from that of long paths. While training on long-hop data can improve corresponding performance, collecting it at scale is difficult and expensive. Hence, we explore the short-to-long generalization scenario for the first time, which aims to enhance the model's capability to handle long-hop questions using easily collected short-hop reasoning data. In this scenario, the reasoning model can only learn the features of short-hop questions due to the lack of long-hop data, and thus two challenges emerge. (1) Struggling to determine where to go next : as the number of steps increases, the model needs to reason about the next step based on a long context composed of more evidences. This reasoning process will confuse the reasoning model, which can only learn the pattern from a short context to the next step. (2) Hard to determine when to stop reasoning : too many reasoning hops may introduce excessive noise, while too few may fail to gather sufficient supporting information. The path lengths of long-hop questions vary significantly. Thus, a fixed stopping threshold, commonly used for short-hop questions with similar path lengths, cannot handle them effectively. Inspired by the classic recursive algorithm, we propose a novel Recursion-based Short-to-Long Generalization (RSLG) reasoning framework, which recursively decomposes long-hop questions into multiple short-hop questions that can be handled by a short-hop reasoning model. In this way, the above two challenges are transformed into how to decompose and when to stop decomposition. For how to decompose, we introduce the parallel and sequential recursion patterns to guide the recursive decomposition processes. A local-to-global recursive demonstration construction strategy is proposed to obtain the corresponding recursive demonstrations from the perspective of certainty, complexity, and diversity, respectively. For when to stop decomposition, we propose three recursive termination criteria in view of decomposability, redundancy, and relevance. Extensive experiments on six short-to-long settings across diverse domains demonstrate RSLG's superior performance, robustness, and efficiency in this challenging scenario. The code and data link: https://github.com/NLPGM/RSLG