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OpenGenAlign: A Preference Dataset and Benchmark for Trustworthy Reward Modeling in Open-Ended, Long-Context Generation

arXiv — cs.CL•Thursday, November 13, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The introduction of OpenGenAlign marks a significant step forward in the field of artificial intelligence, particularly in the realm of large language models (LLMs). This framework and dataset, comprising 33,000 high-quality preference data points with an impressive 81% human agreement rate, are designed to tackle the challenges of reward modeling in open-ended, long-context generation. Previous reward models have struggled with performance, often yielding suboptimal results in evaluating LLM outputs. OpenGenAlign aims to rectify this by providing a robust benchmark that enhances the evaluation process. Experimental results indicate that the trained reward model outperforms existing models, effectively improving the generation quality of policy models through reinforcement learning. This development is crucial as it not only enhances the safety and helpfulness of LLMs but also broadens their applicability in complex tasks such as long-context question answering, data-to-text generation…
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