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Text-to-Image Generative Modeling Without Language Pretraining

Paper recorded by Signals 4 on 2026-10-19 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-19 on arXiv · recorded by Signals 4 on 2026-10-20

Category: cs.LG · 机器学习 · first seen 2026-10-20

Abstract

Modern text-to-image (T2I) systems use internet-scale language pretraining for text-conditioned image generation. We ask whether language pretraining is necessary for strong T2I generation. We introduce Halcyon, a family of early-fusion multimodal autoregressive models pretrained entirely from scratch on GPIC, a dataset of 100M image-text pairs. Halcyon-0.52B outperforms LlamaGen-XL/8 (48.0 vs. 63

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#234 most recent of 749 cs.LG papers we have recorded · ↑ newer: Learning Computational Structure for Sample-Efficient Prediction · ↓ older: Newton-Based Methods for Bilevel Optimization Beyond Lower-Level Stron
Cite this page: Text-to-Image Generative Modeling Without Language Pretraining: the #234 most recent of 749 cs.LG papers we have recorded (as of 2026-10-19). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/text-to-image-generative-modeling-without-language-pretraining.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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