The agent possesses capabilities beyond conversation, including reflection, tool usage, and expert consultation.

There has been significant interest in "extreme" compression of large language models (LLMs), i.e., to 1-2 bits per parameter, which allows such models to be executed efficiently on resource-constrained devices.

This paper proposes ProEdit - a simple yet effective framework for high-quality 3D scene editing guided by diffusion distillation in a novel progressive manner.

We study a typical optimization model where the optimization variable is composed of multiple probability distributions. Though the model appears frequently in practice, such as for policy problems, it lacks specific analysis in the general setting.

Unfortunately, collecting high-quality and diverse data is both expensive and time-consuming. To mitigate this issue, we propose a novel Star-Agents framework, which automates the enhancement of data quality across datasets through multi-agent collaboration and assessment. The framework adopts a three-pronged strategy.

We compare to state-of-the-art imitation learning and L VM baselines and see that QueST's architecture leads to strong performance on several multitask and few-shot learning benchmarks.

The pressure for survival prohibits slow, linear adaptation to different goals, i.e., learning value functions from scratch for each new objective.