Large language models (LLMs) are impressive, but aligning them with human preferences can be tricky. Think of it like trying to teach a brilliant but easily distracted student: you want them to focus their skills on the right tasks. Current methods, like Reinforcement Learning from Human Feedback (RLHF), often struggle to balance improvement in target areas with maintaining performance in other areas—like keeping our student from neglecting other subjects while they master math. This is where “BoNBoN Alignment” comes in, offering a novel approach with some sweet results. The research delves into the “best-of-n” or BoN sampling strategy, where an LLM generates multiple responses, and we pick the best one. It's like having the student try several approaches to a problem and choosing the most successful. The paper finds that BoN is remarkably effective, offering near-optimal performance in balancing desired output with minimal off-target changes. But BoN has a catch—it's computationally expensive, like asking the student to work through dozens of problems for every single assignment. That’s where the “BoNBoN” part comes in. This innovative technique trains a new LLM to *mimic* the BoN process, effectively learning from the best while avoiding the extra work during inference. BoNBoN Alignment achieves this by strategically combining samples representing the best and worst outputs generated by the original LLM, guiding the new model toward optimal performance. Experiments show that BoNBoN outperforms other alignment techniques, achieving higher “win rates” against the base LLM while minimizing off-target deviations. In simpler terms, it’s like the student has internalized the selection process and now consistently produces high-quality work without needing to try multiple versions. BoNBoN Alignment is a promising direction for improving LLMs. By efficiently combining the benefits of best-of-n sampling with focused training, it addresses key challenges in LLM alignment, paving the way for smarter, more human-aligned AI.