665 citations · 2.3k across the 48 of their papers we have counts for
4 papers · 1 filter
The CRINGE Loss: Learning what language not to model
Leonard Adolphs, Tianyu Gao, Jing Xu +3
Standard language model training employs gold human documents or human-human interaction data, and treats all training data as positive examples. Growing evidence shows that even w…
Walk the Random Walk: Learning to Discover and Reach Goals Without Supervision
Lina Mezghani, Sainbayar Sukhbaatar, Piotr Bojanowski +1
Learning a diverse set of skills by interacting with an environment without any external supervision is an important challenge. In particular, obtaining a goal-conditioned agent th…
DIRECTOR: Generator-Classifiers For Supervised Language Modeling
Kushal Arora, Kurt Shuster, Sainbayar Sukhbaatar +1
Current language models achieve low perplexity but their resulting generations still suffer from toxic responses, repetitiveness and contradictions. The standard language modeling…
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL
Akram Erraqabi, Marlos C. Machado, Mingde Zhao +4
In reinforcement learning, the graph Laplacian has proved to be a valuable tool in the task-agnostic setting, with applications ranging from skill discovery to reward shaping. Rece…