arXiv Artificial Intelligence

Reward-Decomposed Reinforcement Learning for Immersive Video Role-Playing

2 hours ago 28.09.2026 07:00 👁 2 views
Reward-Decomposed Reinforcement Learning for Immersive Video Role-Playing

Quick summary

arXiv:2605.04733v3 Announce Type: replace Abstract: Text-based role-playing models can imitate character styles, but often fail to capture scene atmosphere and evolving tension, which are crucial for immersive applications such as VR games and interactive narratives. We study video-grounded role-playing dialogue and introduce EBM-RL (Eye--Brain--Mouth Reinforcement Learning), a decoupled GRPO-based framework that separates observation (), reasoning (), and utterance generation (). This design mimics the human See-Think-Speak process, enabling the model to ground dialogue in visual perception b

Key takeaways

  • arXiv:2605.04733v3 Announce Type: replace Abstract: Text-based role-playing models can imitate character styles, but often fail to capture scene atmosphere and evolving tension, which are crucial for immersive applications such as VR games and interactive narratives.
  • We study video-grounded role-playing dialogue and introduce EBM-RL (Eye--Brain--Mouth Reinforcement Learning), a decoupled GRPO-based framework that separates observation (), reasoning (), and utterance generation ().
  • This design mimics the human See-Think-Speak process, enabling the model to ground dialogue in visual perception b

Why it matters

The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗