14 citations · 22 across the 9 of their papers we have counts for
11 papers
Training LLM Agents to Empower Humans
Evan Ellis, Vivek Myers, Jens Tuyls +3
Assistive agents should not only take actions on behalf of a human, but also step out of the way and cede control when there are important decisions to be made. However, current me…
Representation-Based Exploration for Language Models: From Test-Time to Post-Training
Jens Tuyls, Dylan J. Foster, Akshay Krishnamurthy +1
Reinforcement learning (RL) promises to expand the capabilities of language models, but it is unclear if current RL techniques promote the discovery of novel behaviors, or simply s…
Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents
Davide Paglieri, Bartłomiej Cupiał, Jonathan Cook +6
Training large language models (LLMs) to reason via reinforcement learning (RL) significantly improves their problem-solving capabilities. In agentic settings, existing methods lik…
Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning
Chongyi Zheng, Jens Tuyls, Joanne Peng +1
Self-supervised learning has the potential of lifting several of the key challenges in reinforcement learning today, such as exploration, representation learning, and reward design…
Language-Guided World Models: A Model-Based Approach to AI Control
Alex Zhang, Khanh Nguyen, Jens Tuyls +2
This paper introduces the concept of Language-Guided World Models (LWMs) -- probabilistic models that can simulate environments by reading texts. Agents equipped with these models…
Scaling Laws for Imitation Learning in Single-Agent Games
Jens Tuyls, Dhruv Madeka, Kari Torkkola +3
Imitation Learning (IL) is one of the most widely used methods in machine learning. Yet, many works find it is often unable to fully recover the underlying expert behavior, even in…
Multi-Stage Episodic Control for Strategic Exploration in Text Games
Jens Tuyls, Shunyu Yao, Sham Kakade +1
Text adventure games present unique challenges to reinforcement learning methods due to their combinatorially large action spaces and sparse rewards. The interplay of these two fac…
Differentially Private Language Models Benefit from Public Pre-training
Gavin Kerrigan, Dylan Slack, Jens Tuyls
Language modeling is a keystone task in natural language processing. When training a language model on sensitive information, differential privacy (DP) allows us to quantify the de…
Gradient-based Analysis of NLP Models is Manipulable
Junlin Wang, Jens Tuyls, Eric Wallace +1
Gradient-based analysis methods, such as saliency map visualizations and adversarial input perturbations, have found widespread use in interpreting neural NLP models due to their s…
Generative Modeling for Atmospheric Convection
Griffin Mooers, Jens Tuyls, Stephan Mandt +2
While cloud-resolving models can explicitly simulate the details of small-scale storm formation and morphology, these details are often ignored by climate models for lack of comput…
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Eric Wallace, Jens Tuyls, Junlin Wang +3
Neural NLP models are increasingly accurate but are imperfect and opaque---they break in counterintuitive ways and leave end users puzzled at their behavior. Model interpretation m…