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Found 8 papers, 4 papers with code
Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning
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
In this work, we take initial steps in developing robust LWMs that can generalize to compositionally novel language descriptions.
Scaling Laws for Imitation Learning in Single-Agent Games
Inspired by recent work in Natural Language Processing (NLP) where "scaling up" has resulted in increasingly more capable LLMs, we investigate whether carefully scaling up model and data size can bring similar improvements in the imitation learning setting for single-agent games.
Multi-Stage Episodic Control for Strategic Exploration in Text Games
Text adventure games present unique challenges to reinforcement learning methods due to their combinatorially large action spaces and sparse rewards.
Gradient-based Analysis of NLP Models is Manipulable
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 simplicity, flexibility, and most importantly, their faithfulness.
Differentially Private Language Models Benefit from Public Pre-training
Language modeling is a keystone task in natural language processing.
Generative Modeling for Atmospheric Convection
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 computational resources.
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Neural NLP models are increasingly accurate but are imperfect and opaque---they break in counterintuitive ways and leave end users puzzled at their behavior.
