Deep learning has become the dominant approach in artificial intelligence to solve complex
data-driven problems. Originally applied almost exclusively in computer-science areas such …

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state …

Abstract We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose
approach for uncertainty representation and calibration in deep learning. Stochastic Weight …

Transformers have significantly impacted domains like natural language processing,
computer vision, and robotics, where they improve performance compared to other neural …

In this paper we consider deterministic policy gradient algorithms for reinforcement learning
with continuous actions. The deterministic policy gradient has a particularly appealing form …

Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods,
have been shown to improve a neural network's generalization performance over stochastic …

Combinatorial optimization (CO) problems are notoriously challenging for neural networks,
especially in the absence of labeled instances. This work proposes an unsupervised …

The foundation and emphasis of robotic manipulator control is Inverse Kinematics (IK). Due
to the complexity of derivation, difficulty of computation, and redundancy, traditional IK …

Reinforcement learning from human feedback (RLHF) aligns large language models (LLMs)
by encouraging their generations to have high rewards, using a reward model trained on …

Deep reinforcement learning agents for continuous control are known to exhibit significant
instability in their performance over time. In this work, we provide a fresh perspective on …