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Found 9 papers, 2 papers with code
Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation
Text-to-image (T2I) generation has made significant advances in recent years, but challenges still remain in the generation of perceptual artifacts, misalignment with complex prompts, and safety.
Preference Adaptive and Sequential Text-to-Image Generation
Our Preference Adaptive and Sequential Text-to-image Agent (PASTA) extends T2I models with adaptive multi-turn capabilities, fostering collaborative co-creation and addressing uncertainty or underspecification in a user's intent.
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
We focus on diffusion models, defining the fine-tuning task as an RL problem, and updating the pre-trained text-to-image diffusion models using policy gradient to maximize the feedback-trained reward.
Aligning Text-to-Image Models using Human Feedback
Our results demonstrate the potential for learning from human feedback to significantly improve text-to-image models.
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning
Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carry on rich open-ended conversations with humans "in the wild" remains a formidable challenge.
A Mixture-of-Expert Approach to RL-based Dialogue Management
Despite recent advancements in language models (LMs), their application to dialogue management (DM) problems and ability to carry on rich conversations remain a challenge.
Variational Model-based Policy Optimization
Model-based reinforcement learning (RL) algorithms allow us to combine model-generated data with those collected from interaction with the real system in order to alleviate the data efficiency problem in RL.
CAQL: Continuous Action Q-Learning
Value-based reinforcement learning (RL) methods like Q-learning have shown success in a variety of domains.
