Recent methodologies in LLM self-training mostly rely on LLM generating responses and filtering those with correct output answers as training data. This approach often yields a low-quality fine-tuning training set (e.g., incorrect plans or intermediate reasoning).

Easily integrated, FamO2O statistically enhances existing algorithms' performance.

In these systems, there are two sides: queues (agents) on one side and servers (arms) on the other.

However, these results can be too general to be applicable to natural data distributions. Indeed, humans are quite robust for tasks involving vision.

In reinforcement learning (RL), agents sequentially interact with a changing environment, aiming to collect as much reward as possible.

We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark.

To address the above issues, we propose CrossGNN, a linear complexity GNN model to refine the cross-scale and cross-variable interaction for MTS. To deal with the unexpected noise in time dimension, an adaptive multi-scale identifier (AMSI) is leveraged to construct multi-scale time series with reduced noise.