标题： 自回归图像编辑中强化学习的潜力 显示英文标题

标题： The Promise of RL for Autoregressive Image Editing

摘要： 我们探索了三种策略以在广泛的图像编辑任务中提升性能：监督微调（SFT）、强化学习（RL）和思维链（CoT）推理。 为了在一个一致的框架中研究所有这些组件，我们采用了一个自回归多模态模型，以统一的方式处理文本和视觉标记。 我们发现将RL与大型多模态LLM验证器结合使用是最有效的策略。 因此，我们发布了EARL：基于自回归和RL的编辑，一个强大的基于RL的图像编辑模型，在与强大基线的比较中表现出色，尽管使用的训练数据要少得多。 因此，EARL推动了自回归多模态模型在图像编辑领域的前沿。 我们在https://github.com/mair-lab/EARL发布我们的代码、训练数据和训练好的模型。 显示英文摘要

摘要： We explore three strategies to enhance performance on a wide range of image editing tasks: supervised fine-tuning (SFT), reinforcement learning (RL), and Chain-of-Thought (CoT) reasoning. In order to study all these components in one consistent framework, we adopt an autoregressive multimodal model that processes textual and visual tokens in a unified manner. We find RL combined with a large multi-modal LLM verifier to be the most effective of these strategies. As a result, we release EARL: Editing with Autoregression and RL, a strong RL-based image editing model that performs competitively on a diverse range of edits compared to strong baselines, despite using much less training data. Thus, EARL pushes the frontier of autoregressive multimodal models on image editing. We release our code, training data, and trained models at https://github.com/mair-lab/EARL.