Proact-VL2: Reinforcement Learning for Open-Ended Proactive Real-Time Streaming Interaction

As AI companions increasingly participate in shared interactive experiences, such as co-watching and commenting on a game video with a user, they must continuously process streaming inputs to determine when and how to contribute. However, existing proactive real-time streaming models primarily optimize for predefined targets via supervised fine-tuning, such as flagging specific events or answering set queries, which falls short in open-ended scenarios, as it fails to ensure that the interaction trajectory remains contextually grounded, dynamically adapts to shifting conversational dynamics, and stays well-coordinated with others. We address this gap with reinforcement learning using a reward that assesses contextual grounding, adaptability, and human-AI coordination. Experiments demonstrate that this approach outperforms supervised learning and reference-based rewards, substantially improving proactive interaction quality and reducing multimodal grounding errors while preserving general video understanding capabilities.