Policy-based Tuning of Autoregressive Image Models with Instance- and Distribution-Level Rewards
Autoregressive (AR) models are highly effective for image generation, yet their standard maximum-likelihood estimation training lacks direct optimization for sample quality and diversity. While reinforcement learning (RL) has been used to align diffusion models, these methods typically suffer from output diversity collapse. Similarly, concurrent RL methods for AR models rely strictly on instance-level rewards, often trading off distributional coverage for quality. To address these limitations, we propose a lightweight RL framework that casts token-based AR synthesis as a Markov Decision Process, optimized via Group Relative Policy Optimization (GRPO). Our core contribution is the introduc...
| Authors | Orhun Bugra Baran, Melih Kandemir, Ramazan Gokberk Cinbis |
|---|---|
| Primary category | cs.LG |
| Published | 24 March 2026 |
| Last updated at source | 9 July 2026 |
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