The article "LongRM: Revealing and Unlocking the Context Boundary of Reward Modeling," published on arXiv, emphasizes the critical role of reward modeling in aligning large language models (LLMs) with human preferences, particularly in scenarios involving long history trajectories (F1). It points out that current reward models predominantly focus on short contexts, which limits their effectiveness in evaluating model responses over extended interactions (F2). The article advocates for evaluation criteria that assess not only the quality of responses but also their consistency with the provided context, addressing a significant gap in existing methodologies (F3). This approach aims to improve the alignment of LLM outputs with user expectations by ensuring that responses remain coherent and contextually relevant throughout longer conversations. The discussion aligns with recent research trends on arXiv that explore the challenges of reward modeling in LLMs and the importance of context-aware evaluation frameworks. By revealing and unlocking the boundaries of context in reward modeling, the article contributes to advancing more robust and reliable alignment techniques for future AI applications.