## Introduction The world of video reward models is a rapidly evolving field. However, current models are vulnerable to a range of issues, including 'reward hacking' and annotation noise, which can lead to imprecise and ineffective video generation outcomes. To address these limitations, Meta has developed a new framework called SoliReward. The system is designed to mitigate the vulnerability of video reward models and provide more precise preference signals. ## How SoliReward works cSoliReward uses a binary annotation strategy, where each video is assigned a positive or negative label. This helps reduce annotation noise and provides more precise preferences. In addition, SoliReward employs a feature aggregation technique to combine information from multiple video reward models. This enables the generation of more accurate and reliable outcomes. ## Benefits of SoliReward cSoliReward offers several significant benefits, including: * Reduced risk of 'reward hacking' * Reduced annotation noise * More precise preference signals * Improved security of video reward models ## Conclusion cIn conclusion, SoliReward represents a major breakthrough for the world of video reward models. The system is designed to mitigate the vulnerability of current models and provide more precise preferences. We are excited to see how this new framework will develop in the future. ## References cFor further information on SoliReward, please visit Meta's official website or consult the technical documentation available online.