3 accepted papers
Reinforcement learning (RL) has become a trending paradigm for training large language models (LLMs), particularly for reasoning tasks. Effective RL for LLMs requires massive parallelization and poses an urgent need for efficient training systems. Most existing large-scale RL systems for LLMs are sy…
Neural 3D modeling and novel view synthesis with Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) typically requires the multi-view images with wide baselines and accurate camera poses as input. However, scenarios with accidental camera motions are rarely studied. In this paper, we prop…
Novel view synthesis with neural models has advanced rapidly in recent years, yet adapting these models to scene changes remains an open problem. Existing methods are either labor-intensive, requiring extensive model retraining, or fail to capture detailed types of changes over time. In this paper,…