Large Language Models (LLMs) have made incredible strides in various tasks, but their reasoning abilities often fall short of human capabilities. Think about how we solve problems: we don't just go from question to answer. We also work backward, checking our logic by reversing the steps. This “reverse thinking” is key to how humans reason effectively. New research explores how to empower LLMs with this same ability. The approach, called Reverse-Enhanced Thinking (REVTHINK), involves training LLMs to not only answer questions but also to generate “backward questions” and solve them. Imagine an LLM not just calculating 2 + 3 = 5, but also asking, 'If the total is 5 and one number is 2, what's the other?' This process of bidirectional thinking allows the model to verify its reasoning, much like a human double-checking their work. REVTHINK has shown impressive results across diverse reasoning tasks, including commonsense, math, and logic. It has significantly outperformed standard training methods, boosting accuracy by an average of 13.53%. What's even more exciting is its sample efficiency: using only 10% of the typical training data, REVTHINK outperforms models trained on the full dataset. This suggests that reverse thinking is a powerful tool for enhancing LLM reasoning and making them more robust learners. While there's still much to explore, this research points to a future where LLMs can reason more like humans, not just mimicking patterns but truly understanding the underlying logic. This breakthrough could revolutionize how we use LLMs, opening doors to more complex problem-solving and deeper comprehension.