The field of language generation is moving towards safer and more aligned models, with a focus on multi-objective optimization and the integration of human values and safety constraints. Recent work has shown that reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO) can be effective in steering models towards desired outcomes, but also introduce complexity and potential biases. To address these challenges, researchers are exploring new frameworks such as Group Relative Policy Optimization (GRPO) and Ex-Ante Reasoning Preference Optimization (ERPO), which prioritize safety, efficiency, and transparency. Notably, the development of high-quality safety datasets, such as STAR-1, is also playing a crucial role in advancing the field. Noteworthy papers include: Optimizing Safe and Aligned Language Generation: A Multi-Objective GRPO Approach, which proposes a GRPO framework for safe and aligned language generation. SACA: A Scenario-Aware Collision Avoidance Framework for Autonomous Vehicles Integrating LLMs-Driven Reasoning, which introduces a scenario-aware collision avoidance framework for autonomous vehicles. STAR-1: Safer Alignment of Reasoning LLMs with 1K Data, which presents a high-quality safety dataset for large reasoning models. ERPO: Advancing Safety Alignment via Ex-Ante Reasoning Preference Optimization, which proposes a novel safety alignment framework that equips LLMs with explicit preemptive reasoning.