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Found 6 papers, 2 papers with code
Transformers are Sample-Efficient World Models
Deep reinforcement learning agents are notoriously sample inefficient, which considerably limits their application to real-world problems.
Ranked #7 on
Atari Games 100k
on Atari 100k
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned
With this in mind, we hosted the third edition of the MineRL ObtainDiamond competition, MineRL Diamond 2021, with a separate track in which we permitted any solution to promote the participation of newcomers.
Language Models are Few-Shot Butlers
Pretrained language models demonstrate strong performance in most NLP tasks when fine-tuned on small task-specific datasets.
Structural analysis of an all-purpose question answering model
Attention is a key component of the now ubiquitous pre-trained language models.
On the importance of pre-training data volume for compact language models
Recent advances in language modeling have led to computationally intensive and resource-demanding state-of-the-art models.
Ranked #7 on
Question Answering
on FQuAD
Multi-task Reinforcement Learning with a Planning Quasi-Metric
We introduce a new reinforcement learning approach combining a planning quasi-metric (PQM) that estimates the number of steps required to go from any state to another, with task-specific "aimers" that compute a target state to reach a given goal.
