These sources discuss QALIGN, a new technique for aligning Large Language Model (LLM) outputs with desired preferences at the time of generating the response, rather than requiring expensive prior training or fine-tuning of the model weights. The core idea is to use Markov chain Monte Carlo (MCMC) sampling, guided by an external reward model (RM), to iteratively refine outputs and converge towards a preferred distribution. This approach offers significant resource efficiency compared to traditional methods like Reinforcement Learning from Human Feedback (RLHF) or Direct Preference Optimization (DPO), potentially making advanced alignment research more accessible, particularly in environments like business schools. The text highlights QALIGN's ability to leverage human-in-the-loop or AI feedback via RMs and shifts the research focus from model architecture to the crucial areas of reward model design and evaluation. Experiments demonstrate that QALIGN can outperform existing test-time methods like best-of-n (BoN) and majority voting (MV), showing consistent performance improvements as computational resources increase, unlike methods that can degrade from over-optimizing imperfect RMs.