Large language models (LLMs) excel at various tasks, but complex reasoning remains a challenge. While training LLMs with explicit reasoning examples helps, obtaining these examples often requires expensive human supervision or access to even more powerful, restricted models. A new research paper from Salesforce AI Research introduces "ReGenesis," a method that allows LLMs to generate their own reasoning examples for self-improvement. Existing self-training methods often struggle with generalizing to different types of reasoning problems. The researchers found that these methods create reasoning examples too closely tied to the specific task they’re trained on. They lack the flexibility to apply to new situations. ReGenesis overcomes this limitation by teaching LLMs to reason in stages, moving from abstract principles to concrete problem-solving steps. This approach generates more diverse and general reasoning paths, useful for a wider range of tasks. Think of it like learning to cook. Instead of just memorizing specific recipes, ReGenesis helps LLMs understand underlying cooking principles like balancing flavors or adjusting cooking times based on ingredients. This broader understanding allows them to adapt to new recipes and even create their own culinary masterpieces. In experiments, ReGenesis consistently outperformed existing methods on both familiar and unfamiliar reasoning tasks. This suggests it can effectively transform LLMs into more adaptable, generalized reasoning machines. The ability of LLMs to generate their own training data opens exciting possibilities. This reduces the need for costly human supervision and could lead to more efficient, adaptable AI systems. However, ensuring the quality and reliability of self-generated data remains a crucial area of ongoing research. The future of LLMs likely lies in their ability to continuously learn and adapt, and ReGenesis represents a significant stride in that direction.