Effective resistances have a broad range of algorithmic implications.

However, current Vid-LLMs struggle to simultaneously retain high-quality frame-level semantic information ( i.e., a sufficient

The second step is reward modeling, which is the origin of the name "reward-based".

Reinforcement Learning from Human Feedback (RLHF) has been pivotal in aligning Large Language Models with human values but often suffers from overopti-mization due to its reliance on a proxy reward model.