193 citations · 746 across the 16 of their papers we have counts for
14 papers · 1 filter
Fine-tuning language models to find agreement among humans with diverse preferences
Michiel A. Bakker, Martin J. Chadwick, Hannah R. Sheahan +8
Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static…
Synthetic Returns for Long-Term Credit Assignment
David Raposo, Sam Ritter, Adam Santoro +5
Since the earliest days of reinforcement learning, the workhorse method for assigning credit to actions over time has been temporal-difference (TD) learning, which propagates credi…
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents
Jane X. Wang, Michael King, Nicolas Porcel +14
There has been rapidly growing interest in meta-learning as a method for increasing the flexibility and sample efficiency of reinforcement learning. One problem in this area of res…
Rapid Task-Solving in Novel Environments
Sam Ritter, Ryan Faulkner, Laurent Sartran +3
We propose the challenge of rapid task-solving in novel environments (RTS), wherein an agent must solve a series of tasks as rapidly as possible in an unfamiliar environment. An ef…
MEMO: A Deep Network for Flexible Combination of Episodic Memories
Andrea Banino, Adrià Puigdomènech Badia, Raphael Köster +7
Recent research developing neural network architectures with external memory have often used the benchmark bAbI question and answering dataset which provides a challenging number o…
Stabilizing Transformers for Reinforcement Learning
Emilio Parisotto, H. Francis Song, Jack W. Rae +10
Owing to their ability to both effectively integrate information over long time horizons and scale to massive amounts of data, self-attention architectures have recently shown brea…