arXiv:2603.04976v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards ( RLVR ) has emerged as a transformative paradigm for enhancing the reasoning capabilities of Large Language Models ( LLMs), yet its potential in 3D scene understanding remains under-explored. Existing approaches largely rely on Supervised Fine-Tuning ( SFT), where the token-level cross-entropy loss acts as an indirect proxy for optimization, leading to a misalignment between training objectives and task performances. To bridge this gap, we present Reinforcement Fine-Tuning for Video-based
The development of more sophisticated AI models and the increasing demand for advanced 3D scene understanding in various applications necessitate more effective training paradigms beyond Supervised Fine-Tuning.
Improving 3D scene understanding via reinforcement fine-tuning can significantly enhance autonomous systems and AI agents operating in complex real-world environments, leading to more robust and reliable applications.
The optimization of 3D scene understanding models will transition from indirect proxy losses to objective-aligned reinforcement learning, potentially leading to a new standard for training vision models.
- · AI researchers and developers
- · Robotics and autonomous vehicles
- · Computer vision applications
- · AI agents
- · Companies reliant solely on Supervised Fine-Tuning
- · Legacy 3D scene understanding methods
Reinforcement Fine-Tuning (RFT) becomes a new benchmark for training video-based 3D scene understanding models.
Enhanced 3D perception capabilities enable significant advancements in autonomous navigation, virtual reality, and human-robot interaction.
The broader adoption of RFT could influence the development paradigms for other complex AI tasks requiring sophisticated reasoning and environmental interaction.
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Read at arXiv cs.AI