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Found 3 papers, 1 papers with code
REBEL: Reinforcement Learning via Regressing Relative Rewards
While originally developed for continuous control problems, Proximal Policy Optimization (PPO) has emerged as the work-horse of a variety of reinforcement learning (RL) applications including the fine-tuning of generative models.
Reviewer2: Optimizing Review Generation Through Prompt Generation
In this paper, we envision a use case where authors can receive LLM-generated reviews that uncover weak points in the current draft.
Shoestring: Graph-Based Semi-Supervised Learning with Severely Limited Labeled Data
More specifically, we address the problem of graph-based semi-supervised learning in the presence of severely limited labeled samples, and propose a new framework, called {\em Shoestring}, that improves the learning performance through semantic transfer from these very few labeled samples to large numbers of unlabeled samples.
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