Diffusion language models (DLMs) are theoretically efficient but are limited by fixed-length decoding and poor compatibility with KV caches. Block diffusion mitigates these issues, yet still enforces a fixed block size and costly training. We introduce Next Sequence Prediction (NSP), which unifies next token and next block prediction, enabling the model to adaptively determine generation length at each step. NSP reduces to standard next token prediction when the length is one. Building on NSP, we propose the Sequential Diffusion Language Model (SDLM), which retrofits pretrained autoregressive language models (ALMs) at minimal cost. Specifically, SDLM performs diffusion inference within fixed-size mask blocks, but dynamically decodes contiguous subsequences based on model confidence, thereby preserving KV-cache compatibility and improving robustness to the varying uncertainty and semantics across the sequence. SDLM matches or surpasses strong autoregressive baselines with only 3.5M training samples, while achieving 2.0× 3.4× higher throughput than Qwen2.5 across three model scales (3B, 7B, 32B) with confidence-based decoding. Furthermore, when sampling with self-speculative decoding, SDLM attains up to 5.7× end-to-end speedup measured as wall-clock tokens per second (TPS). Notably, SDLM-32B achieves performance comparable to Qwen2.5-32B with up to 2.5× speedup, demonstrating the scalability of our approach. Project page and codes: https://github.com/OpenGVLab/SDLM

Recent advancements in open-source multi-modal large language models (MLLMs) have primarily focused on enhancing foundational capabilities, leaving a significant gap in human preference alignment. This paper introduces OmniAlign-V, a comprehensive dataset of 200K high-quality training samples featuring diverse images, complex questions, and varied response formats to improve MLLMs’ alignment with human preferences. We also present MM-AlignBench, a human-annotated benchmark specifically designed to evaluate MLLMs’ alignment with human values. Experimental results show that finetuning MLLMs with OmniAlign-V, using Supervised Fine-Tuning (SFT) or Direct Preference Optimization (DPO), significantly enhances human preference alignment while maintaining or enhancing performance on standard VQA benchmarks, preserving their fundamental capabilities.

While CLIP models are useful for zero-shot vision-and-language (VL) tasks or computer vision tasks, little attention has been paid to the application of CLIP for language tasks. Intuitively, CLIP model have a rich representation pre-trained with natural language supervision, in which we argue that it is useful for language tasks. Hence, this work bridge this gap by investigating a CLIP model for zero-shot text classification. Specifically, we introduce CLIPText, a novel paradigm for zero-shot text classification, which reformulates zero-shot text classification into a text-image matching problem that CLIP can be applied to. In addition, we further incorporate prompt into CLIPText (Prompt-CLIPText) to better derive knowledge from CLIP. Experimental results on seven publicly available zero-shot text classification datasets show that both CLIPText and Prompt-CLIPText attain promising performance. Besides, extensive analysis further verifies that knowledge from CLIP can benefit zero-shot text classification task. We hope this work can attract more breakthroughs on applying VL pre-trained models for language tasks.