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Found 4 papers, 2 papers with code
An Empirical Investigation of Representation Learning for Imitation
We propose a modular framework for constructing representation learning algorithms, then use our framework to evaluate the utility of representation learning for imitation across several environment suites.
RAFT: A Real-World Few-Shot Text Classification Benchmark
Will models soon solve classification tasks that have so far been reserved for human research assistants?
Ranked #2 on
Few-Shot Text Classification
on RAFT
The MineRL BASALT Competition on Learning from Human Feedback
Rather than training AI systems using a predefined reward function or using a labeled dataset with a predefined set of categories, we instead train the AI system using a learning signal derived from some form of human feedback, which can evolve over time as the understanding of the task changes, or as the capabilities of the AI system improve.
Benefits of Assistance over Reward Learning
By merging reward learning and control, assistive agents can reason about the impact of control actions on reward learning, leading to several advantages over agents based on reward learning.
