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Found 7 papers, 4 papers with code
Understanding the Effects of RLHF on LLM Generalisation and Diversity
OOD generalisation is crucial given the wide range of real-world scenarios in which these models are being used, while output diversity refers to the model's ability to generate varied outputs and is important for a variety of use cases.
Meta-Gradients in Non-Stationary Environments
We support these results with a qualitative analysis of resulting meta-parameter schedules and learned functions of context features.
WordCraft: An Environment for Benchmarking Commonsense Agents
This is partly due to the lack of lightweight simulation environments that sufficiently reflect the semantics of the real world and provide knowledge sources grounded with respect to observations in an RL environment.
Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning
Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments.
A Survey of Reinforcement Learning Informed by Natural Language
To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional, relational, and hierarchical structure of the world, and learn to transfer it to the task at hand.
Progress & Compress: A scalable framework for continual learning
This is achieved by training a network with two components: A knowledge base, capable of solving previously encountered problems, which is connected to an active column that is employed to efficiently learn the current task.
Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
Hyperparameter selection generally relies on running multiple full training trials, with selection based on validation set performance.
