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Found 4 papers, 4 papers with code
WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language Models
We propose WorldSense, a benchmark designed to assess the extent to which LLMs are consistently able to sustain tacit world models, by testing how they draw simple inferences from descriptions of simple arrangements of entities.
Building a Subspace of Policies for Scalable Continual Learning
We introduce Continual Subspace of Policies (CSP), a new approach that incrementally builds a subspace of policies for training a reinforcement learning agent on a sequence of tasks.
SaLinA: Sequential Learning of Agents
SaLinA is a simple library that makes implementing complex sequential learning models easy, including reinforcement learning algorithms.
Learning a subspace of policies for online adaptation in Reinforcement Learning
There is a need to develop RL methods that generalize well to variations of the training conditions.
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