The recent advancements in the field of large language model (LLM) alignment and style representation have shown significant progress. Researchers are increasingly focusing on developing methodologies that enhance the reliability and content-independence of LLMs. One notable trend is the exploration of novel techniques to generate and align preferences among wrong options, which has demonstrated improvements in model calibration and correctness. Additionally, there is a growing emphasis on creating synthetic datasets for training style embeddings that are robust and content-independent, leading to superior performance in downstream applications. Another key development is the introduction of anchored alignment methods that improve self-explanation capabilities of LLMs, enhancing both explanation quality and model accuracy. Furthermore, the diversification of contrasting patterns in alignment frameworks is being pursued to create more comprehensive models resistant to jailbreaking attacks. These innovations collectively push the boundaries of LLM capabilities and style representation, fostering advancements in model reliability and performance.