RankE: End-to-End Post-Training for Discrete Text-to-Image Generation with Decoder Co-Evolution
For practitioners of discrete text-to-image generation, this work addresses the fidelity-alignment trade-off in post-training by co-evolving the decoder, a previously overlooked bottleneck.
Discrete autoregressive text-to-image models suffer from Latent Covariate Shift when only the policy is post-trained, degrading image quality despite improved alignment. RankE co-evolves both the policy and VQ decoder via alternating optimization, achieving simultaneous improvements in FID (15.21) and CLIP (33.76) on MS-COCO 30K with LlamaGen-XL.
Discrete autoregressive (AR) text-to-image (T2I) models pair a VQ tokenizer with an AR policy, and current post-training pipelines optimize only the policy while keeping the VQ decoder frozen. Recent diffusion T2I work, exemplified by REPA-E, has shown that the VAE itself constitutes a key alignment bottleneck, yet no analogous investigation exists for discrete AR models. We show that policy-only optimization induces Latent Covariate Shift: as the policy evolves, the resulting token distribution diverges from the ground-truth distribution on which the decoder was trained, such that reward scores improve while decoded image quality degrades. To address this mismatch, we propose RankE, the first end-to-end post-training framework for discrete T2I generation. Rather than optimizing the policy against a fixed decoder, RankE co-evolves both components through alternating optimization: each module maximizes a ranking-based alignment objective while being regularized by a stability-preserving anchor suited to its parameter space. This co-evolution breaks the fidelity--alignment trade-off that plagues frozen-decoder approaches: on LlamaGen-XL (775M), standard RL improves CLIP but degrades FID, whereas RankE improves both simultaneously (FID 15.21, CLIP 33.76 on MS-COCO 30K). Consistent gains on Janus-Pro (1B) confirm that decoder co-evolution reliably converts reward optimization into pixel-space quality improvements.