Large Language Models (LLMs) have achieved remarkable success in instruction-following and dialogue tasks, yet aligning them with human preferences remains a critical challenge. Recent advances such as Direct Preference Optimization (DPO) simplify the alignment pipeline by bypassing explicit reward modeling, but they often suffer from suboptimal reward margin distributions, leading to weak supervision signals and reduced discriminative capacity. In this work, we propose Reward Margin Optimization (RMO), a framework that reshapes reward margin distributions during training to improve alignment performance. RMO comprises three components: (1) a Dual Denoising Filtering strategy that filters ambiguous and noisy preference pairs based on reward margin dynamics; (2) Batch Margin Diversification, which maximizes intra-batch margin variance to enhance learning signal diversity; and (3) Pairwise Margin Amplification, an auxiliary regularization term that encourages larger margins between preferred and dispreferred responses. Extensive experiments on multiple LLMs and datasets demonstrate that RMO consistently improves win rates over strong baselines such as DPO and SimPO, while remaining compatible with various preference-based optimization methods. Our results highlight the critical role of reward margin distribution in preference alignment and establish RMO as an effective and scalable enhancement to existing alignment techniques.

Continual knowledge graph embedding (CKGE) has gained wide attention for managing dynamic knowledge graphs (KGs), which are continuously updated with new facts. Unlike traditional methods designed for static KGs, CKGE enables incremental updates to KG embeddings to accommodate new facts while retaining previously learned knowledge. Despite these advancements, current CKGE studies and benchmarks primarily focus on handling the increasing scale of data while overlooking changes in graph patterns. These changes, altering the graph structure of KGs, are referred to as pattern shifts in this paper. Pattern shifts frequently arise as new facts are added, introducing significant challenges to the stability and adaptability of CKGE methods. To address this gap, we introduce a suite of novel and challenging benchmarks, called PS-CKGE, specifically designed to evaluate CKGE methods under pattern shifts, where logic rules are utilized to capture and manage structural changes in dynamic KGs. Through these benchmarks, we comprehensively evaluate current CKGE methods in terms of their overall performance, resistance to catastrophic forgetting, and adaptability to new knowledge. The results show that pattern shifts not only exacerbate their risk of catastrophic forgetting but also impair their adaptability, usually with greater performance degradation over triples associated with more significant changes.

Knowledge graph embedding (KGE) methods have achieved great success in handling various knowledge graph (KG) downstream tasks. However, KGE methods may learn biased representations on low-quality KGs that are prevalent in the real world. Some recent studies propose adversarial attacks to investigate the vulnerabilities of KGE methods, but their attackers are target-oriented with the KGE method and the target triples to predict are given in advance, which lacks practicability. In this work, we explore untargeted attacks with the aim of reducing the global performances of KGE methods over a set of unknown test triples and conducting systematic analyses on KGE robustness. Considering logic rules can effectively summarize the global structure of a KG, we develop rule-based attack strategies to enhance the attack efficiency. In particular, we consider adversarial deletion which learns rules, applying the rules to score triple importance and delete important triples, and adversarial addition which corrupts the learned rules and applies them for negative triples as perturbations. Extensive experiments on two datasets over three representative classes of KGE methods demonstrate the effectiveness of our proposed untargeted attacks in diminishing the link prediction results. And we also find that different KGE methods exhibit different robustness to untargeted attacks. For example, the robustness of methods engaged with graph neural networks and logic rules depends on the density of the graph. But rule-based methods like NCRL are easily affected by adversarial addition attacks to capture negative rules.