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Found 5 papers, 2 papers with code
Reinforcement Learning Fine-tuning of Language Models is Biased Towards More Extractable Features
Many capable large language models (LLMs) are developed via self-supervised pre-training followed by a reinforcement-learning fine-tuning phase, often based on human or AI feedback.
On The Expressivity of Objective-Specification Formalisms in Reinforcement Learning
However, it is well-known that certain tasks cannot be expressed by means of an objective in the Markov rewards formalism, motivating the study of alternative objective-specification formalisms in RL such as Linear Temporal Logic and Multi-Objective Reinforcement Learning.
Multi-Objective Reinforcement Learning
reinforcement-learning
Goodhart's Law in Reinforcement Learning
First, we propose a way to quantify the magnitude of this effect and show empirically that optimising an imperfect proxy reward often leads to the behaviour predicted by Goodhart's law for a wide range of environments and reward functions.
Lexicographic Multi-Objective Reinforcement Learning
In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems.
Multi-Objective Reinforcement Learning
reinforcement-learning
Privacy-preserving Object Detection
Privacy considerations and bias in datasets are quickly becoming high-priority issues that the computer vision community needs to face.
