VA-π：面向像素感知自回归生成的变分策略对齐
VA-p: Variational Policy Alignment for Pixel-Aware Autoregressive Generation
将自回归生成器的词元似然优化与像素空间重建质量对齐，通过变分ELBO和强化学习策略（以重建质量为内在奖励）实现轻量后训练，可直接用于提升现有AR生成模型（如LlamaGen、Janus-Pro）的生成质量，无需重训分词器或外部奖励模型。
原文依据 VA-\pi formulates the generator-tokenizer alignment as a variational optimization, deriving an evidence lower bound (ELBO) that unifies pixel reconstruction and autoregressive modeling.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论自回归（AR）视觉生成依赖分词器将图像映射为离散序列并还原。然而，分词器被训练为从真实词元重建干净图像，而AR生成器仅针对词元似然进行优化。这种错位导致生成的词元序列可能解码出低质量图像，且缺乏来自像素空间的直接监督。我们提出VA-π，一个轻量级后训练框架，以有原则的像素空间目标直接优化AR模型。VA-π将生成器-分词器对齐形式化为变分优化，推导出一个统一像素重建与自回归建模的证据下界（ELBO）。为在离散词元空间下优化，VA-π引入基于强化的对齐策略，将AR生成器视为策略，以像素空间重建质量作为其内在奖励。奖励通过教师强制下预测词元序列重建原始图像的质量来衡量，从而为模型提供直接的像素级引导，无需昂贵的自由运行采样。ELBO的正则项作为自然正则化器，保持分布一致性。VA-π能够快速适配现有AR生成器，无需重新训练分词器或使用外部奖励模型。仅使用1%的ImageNet-1K数据和25分钟微调，在LlamaGen-XXL上将FID从14.36降至7.65，IS从86.55提升至116.70，同时在GenEval的文本到图像任务上对视觉生成模型（LlamaGen：从0.306到0.339）和统一多模态模型（Janus-Pro：从0.725到0.744）均取得显著增益。
英文原文摘要 1720 字符
Autoregressive (AR) visual generation relies on tokenizers to map images to and from discrete sequences. However, tokenizers are trained to reconstruct clean images from ground-truth tokens, while AR generators are optimized only for token likelihood. This misalignment leads to generated token sequences that may decode into low-quality images, without direct supervision from the pixel space. We propose VA-\boldsymbol \pi , a lightweight post-training framework that directly optimizes AR models with a principled pixel-space objective. VA-\pi formulates the generator-tokenizer alignment as a variational optimization, deriving an evidence lower bound (ELBO) that unifies pixel reconstruction and autoregressive modeling. To optimize under the discrete token space, VA-\pi introduces a reinforcement-based alignment strategy that treats the AR generator as a policy, uses pixel-space reconstruction quality as its intrinsic reward. The reward is measured by how well the predicted token sequences can reconstruct the original image under teacher forcing, giving the model direct pixel-level guidance without expensive free-running sampling.The regularization term of the ELBO serves as a natural regularizer, maintaining distributional consistency. VA-\pi enables rapid adaptation of existing AR generators, without neither tokenizer retraining nor external reward models. With only 1% ImageNet-1K data and 25 minutes of tuning, it reduces FID from 14.36 to 7.65 and improves IS from 86.55 to 116.70 on LlamaGen-XXL, while also yielding notable gains in the text-to-image task on GenEval for both visual generation model (LlamaGen: from 0.306 to 0.339) and unified multi-modal model (Janus-Pro: from 0.725 to 0.744).