The field of large language models is moving towards more nuanced and human-aligned preference learning and alignment. Recent developments focus on addressing the limitations of existing methods, such as overconfidence and neglect of counterfactual prompts. New approaches, including abductive preference learning and adaptive intent-driven preference optimization, aim to improve the sensitivity of models to prompt differences and capture diverse user intentions. Additionally, there is a growing interest in developing methods that can infer users' deep implicit preferences and enable defensive reasoning to navigate real-world ambiguity. Noteworthy papers include: A-IPO, which introduces an intention module to infer latent user intent and incorporates it into the reward function, resulting in clearer separation between preferred and dispreferred responses. Aligning Deep Implicit Preferences by Learning to Reason Defensively, which proposes Critique-Driven Reasoning Alignment to bridge the preference inference gap and instill defensive reasoning. From Literal to Liberal, which presents a meta-prompting framework for eliciting human-aligned exception handling in large language models, achieving a 95% Human Alignment Score.