Large language models (LLMs) have revolutionized how we interact with technology, but they sometimes stumble when faced with complex instructions involving specific constraints like length or format. Imagine asking an LLM to write a 500-word essay on a particular topic in a formal tone—it might generate a great piece, but not necessarily hit the specified word count or maintain the desired style. This challenge arises because current instruction-tuning methods rely on training LLMs with complex instruction-response pairs, often generated by other LLMs. However, even the most advanced LLMs can struggle with these complex instructions, leading to inconsistencies in the training data and ultimately impacting the performance of the LLMs being trained. Researchers have devised a clever new technique called "constraint back-translation" to tackle this issue. Instead of relying on other LLMs to generate complex instructions, this method leverages existing high-quality datasets. The key insight is that responses in these datasets often *implicitly* satisfy complex constraints, even if the original instructions were simple. For instance, a response might naturally be a certain length or adhere to a specific format. Constraint back-translation works by identifying these implicit constraints and adding them to the original instructions. This generates high-quality complex instruction-response pairs without relying on the complex instruction-following abilities of other LLMs, thereby reducing noise and cost. This research has led to the creation of CRAB, a new dataset of complex instruction-response pairs. Experiments show that LLMs trained on CRAB demonstrate a marked improvement in following complex instructions. In addition to constraint back-translation, researchers have also introduced "reverse training," an auxiliary training objective. Here, the LLM is trained to predict the constraints given the instruction and response, further reinforcing its understanding of these constraints. The impact of these innovations goes beyond just following complex instructions. It also improves general instruction-following capabilities, suggesting an overall enhancement in content quality and coherence. While promising, constraint back-translation has its limitations. Certain constraints, like specifying a particular writing style, require more diverse responses than currently available in training datasets. Future research will focus on refining constraint back-translation and exploring methods to generate even more diverse and nuanced training data. Ultimately, this research contributes significantly to developing more robust and reliable LLMs, capable of handling a broader range of tasks and complex requests with greater accuracy.