Large language models (LLMs) have shown remarkable progress in understanding and generating text, but they've historically struggled with complex reasoning, especially in languages other than English. A technique called "contrastive decoding" has emerged as a way to sharpen an LLM's reasoning abilities by having it compare its initial "amateur" guesses with its final, more refined "expert" answers. However, this method hasn't translated well to multilingual tasks – until now. Researchers discovered a clever way to overcome this limitation using a technique called "layer skipping." Essentially, they found that by strategically bypassing certain layers in the LLM's processing during the initial "amateur" phase, they could create a more effective contrast with the final output. This, in turn, leads to significantly improved reasoning accuracy across a diverse range of languages. Think of it like a student double-checking their work. By deliberately introducing variations in their initial approach, they can better identify and correct errors when comparing it to their final, more polished solution. This layer skipping technique achieves a similar effect within the LLM, enhancing its ability to reason and solve problems. This breakthrough has been tested across several popular LLMs, including Mistral, Baichuan2, Deepseek, LLaMA2, and LLaMA3, and has demonstrated consistent improvements in multilingual reasoning benchmarks. It's a significant step towards truly multilingual AI, opening doors for more accurate and reliable language processing across a wider range of languages. While challenges remain, like the added computational cost, this research paves the way for more sophisticated and versatile language models that can better understand and reason in the world's many languages.