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Found 3 papers, 1 papers with code
Soft Preference Optimization: Aligning Language Models to Expert Distributions
We propose Soft Preference Optimization (SPO), a method for aligning generative models, such as Large Language Models (LLMs), with human preferences, without the need for a reward model.
Automatic Music Playlist Generation via Simulation-based Reinforcement Learning
In this paper, we present a reinforcement learning framework that solves for such limitations by directly optimizing for user satisfaction metrics via the use of a simulated playlist-generation environment.
What to Learn, and How: Toward Effective Learning from Rationales
Learning from rationales seeks to augment model prediction accuracy using human-annotated rationales (i. e. subsets of input tokens) that justify their chosen labels, often in the form of intermediate or multitask supervision.
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