Advancements in instruction tuning and RLHF! Empirical studies.

How the optimization setup of RLHF is limiting the steerability of LLMs.

The report for a small and powerful chat model trained with DPO!

The complicated historical past underpinning reinforcement learning from human feedback!

Where is model-based RL heading 4 years after the seminal paper of my Ph.D.

When you "measure data", you quantify its characteristics to support dataset comparison & curation. You also begin to know what systems will learn. Many ML systems don't reason with this, we posit you should.

My thesis on model-based RL. Let's make models work with tasks!

We propose a new type of documentation for dynamic machine learning (and reinforcement learning) systems!

We detail why reinforcement learning systems pose a different type of (dynamic) risks to society. This paper outlines the different types of feedback present in RL systems, the risks they pose, and a path forward for policymakers.

We flip the script on Offline RL research and ask the question of "what is the best dataset to collect?" rather than "what is the best algorithm?"

In this paper we set out to understand the causes of compounding prediction errors in one-step learned models. With this, we hope a next generation of models can be used to improve model-based reinforcement learning.

A simulator for studying high-agent-count networked systems!

We present a concise set of directions for understanding the societal risks of new directions of AI research.

An open-source PyTorch repository designed from the bottom up for model-based reinforcement learning research.

We showed that advancements in AutoML when paired with common deep RL tasks, MBRL algorithms perform so well they break the simulator.

We study three developing subfields of AI research and their growing relationship with the sociotechnical: AI Safety, Fair Machine Learning, and Human-in-the-loop Autonomy.

We explored how MBRL can learn multi-step, nonlinear controllers!

Trying to reframe the MBRL framework with long-term predictions instead of one-step predictions!

A collections of steps towards a data-driven autonomous microrobot.

Studying the numerical effects of a dual-optimization problem in model-based reinforcement learning -- control and dynamics. When optimizing model accuracy, there is no guarantee on improving task performance!

Learning how to walk with a real-world hexapod using a hierarchy of model-free RL for basic motion primitives with model-based RL for higher level planning.

We used deep model-based reinforcement learning to have a quadrotor learn to hover from less than 5 minutes of all experimental training data.

A collection of steps towards controlled flight of The Ionocraft, a completely silent microrobot with ion thrust!