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Found 4 papers, 3 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.
Dataset Reset Policy Optimization for RLHF
Motivated by the fact that offline preference dataset provides informative states (i. e., data that is preferred by the labelers), our new algorithm, Dataset Reset Policy Optimization (DR-PO), integrates the existing offline preference dataset into the online policy training procedure via dataset reset: it directly resets the policy optimizer to the states in the offline dataset, instead of always starting from the initial state distribution.
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
To overcome this limitation, consistency models proposed learning a new class of generative models that directly map noise to data, resulting in a model that can generate an image in as few as one sampling iteration.
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning
Second-order bounds are instance-dependent bounds that scale with the variance of return, which we prove are tighter than the previously known small-loss bounds of distributional RL.
Distributional Reinforcement Learning
Multi-Armed Bandits
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