扩散大语言模型得到了突飞猛进的发展，早在 25 年 2 月 Inception Labs 推出 Mercury—— 第一个商业级扩散 大型语言模型，同期人民大学发布第一个开源 8B 扩散大语言模型 LLaDA，5 月份 Gemini Diffusion 也接踵 而至。种种迹象表明，扩散大语言模型很可能是下一代大语言模型基础范式的有力竞争者。但是针对于扩 散大语言模型的解码策略和强化学习算法仍然是欠探索的。 近期，复旦大学、上海人工智能实验室、上海交通大学联合研究团队发布最新论文《Taming Masked Diffusion Language Models via Consistency Trajectory Reinforcement Learning with Fewer Decoding Step》。 他们提出了一套对于掩码扩散大语言模型（Masked Diffusion Large Language Model，MDLM）的 高效解码 策略 + 强化学习训练组合 ，显著提升了掩码扩散大语言模型的 推理性能与效率 ，为扩散大语言模型的发展 开辟了新路径。 代码仓库：https://github.com/ ...

然而，最近的一篇论文有了一些意外发现：在数学和编码任务中，任意顺序算法的性能往往不如从左到右采样，或者表现与之相似，而标准的多 token 解码会显著 降低性能。即使仅在两个 token 上进行并行解码，模型在主流基准任务上的性能也会显著下降。 使用 MDLM 进行从左到右的采样是一种适用于推理和编码的高效采样算法。如果没有 [Arriola et al., 2025] 提出的块大小（ block sizes ）来强制形成半自回归（ AR ）的从左到右结构，任意顺序会显著影响 性能。 | Parallel Tokens | GSM8K | | MATH500 | | HumanEval | | Sudoku | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | LLaDA Dream | | | | LLaDA Dream LLaDA Dream LLaDA Dream | | | | | | 76.95 | 75.73 | 33.4 | 29.6 | 16.46 | 51.82 | 47.64 | 61.26 | | 2 | 62.3 ...

Core Insights - Ant Group has officially announced the open-source release of dInfer, the industry's first high-performance inference framework for diffusion language models [1][5] - dInfer demonstrates a significant improvement in inference speed, achieving a 10.7 times increase compared to NVIDIA's Fast-dLLM framework, and reaching a speed of 1011 tokens per second in the HumanEval code generation task [1][4] - The framework addresses key challenges in diffusion language model inference, including high computational costs, KV cache failures, and parallel decoding [1][2] Summary by Sections - **Performance Metrics** - dInfer achieves an average inference speed of 681 tokens per second, compared to 63.6 tokens per second for Fast-dLLM, marking a 10.7 times improvement [4] - When compared to the AR model Qwen2.5-3B, dInfer's average inference speed is 2.5 times faster, at 681 tokens per second versus 277 tokens per second [5] - **Technical Architecture** - dInfer is designed with a modular architecture that includes four core components: Model, KV-Cache Manager, Iteration Manager, and Decoder, allowing developers to customize and optimize their configurations [2] - Each module integrates targeted solutions to overcome the three main challenges faced by diffusion language models [2] - **Industry Impact** - The launch of dInfer signifies a critical step in transitioning diffusion language models from theoretical feasibility to practical efficiency, connecting cutting-edge research with industrial applications [5] - Ant Group invites global developers and researchers to explore the potential of diffusion language models, aiming to build a more efficient and open AI ecosystem [5]

Core Viewpoint - The article discusses the current state of large language models (LLMs) and the challenges they face in achieving Artificial General Intelligence (AGI), emphasizing the need for new paradigms beyond the existing autoregressive (AR) models [4][10][18]. Group 1: Current Challenges in AI Models - Ilya, a prominent AI researcher, warns that data extraction has reached its limits, hindering the progress towards AGI [2][4]. - The existing LLMs often exhibit significant performance discrepancies, with some capable of outperforming human experts while others struggle with basic tasks [13][15]. - The autoregressive model's limitations include a lack of bidirectional modeling and the inability to correct errors during generation, leading to fundamental misunderstandings in tasks like translation and medical diagnosis [26][27][18]. Group 2: New Directions in AI Research - Elon Musk proposes a "purified data" approach to rewrite human knowledge as a potential pathway to AGI [5]. - Researchers are exploring multimodal approaches, with experts like Fei-Fei Li emphasizing the importance of visual understanding as a cornerstone of intelligence [8]. - A new paradigm, the diffusion model, is being introduced by young scholars, which contrasts with the traditional autoregressive approach by allowing for parallel decoding and iterative correction [12][28]. Group 3: Development of LLaDA-MoE - The LLaDA-MoE model, based on diffusion theory, was announced as a significant advancement in the field, showcasing a new approach to language modeling [12][66]. - LLaDA-MoE has a total parameter count of 7 billion, with 1.4 billion activated parameters, and has been trained on approximately 20 terabytes of data, demonstrating its scalability and stability [66][67]. - The model's performance in benchmark tests indicates that it can compete with existing autoregressive models, suggesting a viable alternative path for future AI development [67][71]. Group 4: Future Prospects and Community Involvement - The development of LLaDA-MoE represents a milestone in the exploration of diffusion models, with plans for further scaling and improvement [72][74]. - The team emphasizes the importance of community collaboration in advancing the diffusion model research, similar to the development of autoregressive models [74][79]. - Ant Group's commitment to investing in AGI research reflects a strategic shift towards exploring innovative and potentially high-risk areas in AI [79].

Core Viewpoint - The article discusses the development of the LLaDA-MoE model, the first native MoE architecture diffusion language model trained from scratch, which demonstrates significant performance and efficiency advantages over traditional autoregressive models [2][15][18]. Group 1: Model Development and Performance - The LLaDA-MoE model was trained on 20 terabytes of data and features 1.4 billion active parameters, achieving performance comparable to denser autoregressive models like Qwen2.5-3B while maintaining faster inference speeds [15][17][29]. - The LLaDA series has rapidly evolved, with LLaDA-MoE being a notable milestone, surpassing previous models like LLaDA1.0/1.5 and Dream-7B in various benchmark tests [13][18][29]. - The model's architecture allows for significant scaling potential, with plans to explore higher sparsity ratios and larger MoE diffusion language models [29][40]. Group 2: Technical Innovations and Advantages - The diffusion model approach allows for parallel decoding, bidirectional modeling, and iterative correction, addressing limitations of autoregressive models such as serial bottlenecks and lack of error correction capabilities [38][40]. - Evidence suggests that diffusion language models can achieve better learning outcomes than autoregressive models, particularly in scenarios with limited data, demonstrating a data utilization efficiency that can exceed three times that of autoregressive models [40][41]. - The training framework and infrastructure developed by Ant Group, including the ATorch framework, supports the efficient training of large-scale MoE models [25][26]. Group 3: Strategic Vision and Future Directions - The development of LLaDA-MoE reflects a strategic choice to explore high-potential areas in AI, moving beyond established paths to enhance the limits of intelligence [44][47]. - Ant Group's commitment to innovation is evident in its previous projects and ongoing research in areas like dynamic MoE architectures and hybrid linear architectures, all aimed at achieving general artificial intelligence (AGI) [45][46][47].

Core Viewpoint - The article discusses the advancements in Vision-Language Models (VLMs) and introduces LLaDA-VLA, the first Vision-Language-Action Model developed using large language diffusion models, which demonstrates superior multi-task performance in robotic action generation [1][5][19]. Group 1: Introduction to LLaDA-VLA - LLaDA-VLA integrates Masked Diffusion Models (MDMs) into robotic action generation, leveraging pre-trained multimodal large language diffusion models for fine-tuning and enabling parallel action trajectory prediction [5][19]. - The model architecture consists of three core modules: a vision encoder for RGB feature extraction, a language diffusion backbone for integrating visual and language information, and a projector for mapping visual features to language token space [10][7]. Group 2: Key Technical Innovations - Two major breakthroughs are highlighted: - Localized Special-token Classification (LSC), which reduces cross-domain transfer difficulty by classifying only action-related special tokens, thus improving training efficiency [8][12]. - Hierarchical Action-Structured Decoding (HAD), which explicitly models hierarchical dependencies between actions, resulting in smoother and more reasonable generated trajectories [9][13]. Group 3: Performance Evaluation - LLaDA-VLA outperforms state-of-the-art methods across various environments, including SimplerEnv, CALVIN, and real robot WidowX, achieving significant improvements in success rates and task completion metrics [4][21]. - In specific task evaluations, LLaDA-VLA achieved an average success rate of 58% across multiple tasks, surpassing previous models [15]. Group 4: Experimental Results - The model demonstrated a notable increase in task completion rates and average task lengths compared to baseline models, validating the effectiveness of the proposed LSC and HAD strategies [18][14]. - In a comparative analysis, LLaDA-VLA achieved a success rate of 95.6% in a specific task, significantly higher than other models [14][18]. Group 5: Research Significance and Future Directions - The introduction of LLaDA-VLA establishes a solid foundation for applying large language diffusion models in robotic operations, paving the way for future research in this domain [19][21]. - The design strategies employed in LLaDA-VLA not only enhance model performance but also open new avenues for exploration in the field of embodied intelligence [19].

Group 1 - The article discusses the potential of Diffusion LLMs, particularly Gemini Diffusion, as a significant breakthrough in AI, challenging traditional autoregressive models [3][4][5] - Gemini Diffusion demonstrates high generation efficiency, achieving an average sampling speed of 1479 TPS and up to 2000 TPS in encoding tasks, outperforming Gemini 2.0 Flash-Lite by 4-5 times [4][6] - The parallel generation mechanism of the diffusion architecture allows for efficient processing, which could lead to reduced computational costs compared to autoregressive models [6][7] Group 2 - Mary Meeker emphasizes that the speed of AI development surpasses that of the internet era, highlighting the cost disparity between AI model training and inference [1][2] - The article suggests that the rise of open-source models in China may impact the global supply chain, indicating a shift in competitive dynamics within the industry [1][2] - The balance between computational investment and commercial returns is crucial for enterprises as AI inference costs decline [1][2]

Core Viewpoint - The article discusses the advancements in diffusion language models (dLLMs), particularly focusing on Google's Gemini Diffusion and its implications for AI development, highlighting the speed and performance improvements over traditional autoregressive models [1][8][35]. Group 1: Gemini Diffusion and Its Features - Gemini Diffusion is noted for its impressive generation speed, being five times faster than previous models, and its ability to handle programming tasks effectively [2][8]. - The underlying mechanism of diffusion models allows for rapid iteration and error correction during the generation process, distinguishing it from autoregressive models [2][3]. - Gemini Diffusion's sampling speed can reach an astonishing 1479 tokens per second, showcasing its potential in various benchmarks [8][9]. Group 2: Development of Diffusion Language Models - Prior to Gemini Diffusion, several research teams explored the feasibility of diffusion-based LLMs, including Stanford's Diffusion-LM and Fudan University's DiffusionBERT [3][4]. - The introduction of LLaDA, the first 8 billion parameter diffusion language model, marked a significant milestone in the field, achieving performance comparable to LLaMA 3 [4][21]. - Following LLaDA, other models like d1 and LaViDa have emerged, further establishing LLaDA as a foundational model in dLLM research [20][21]. Group 3: Multimodal Diffusion Language Models - The emergence of diffusion multimodal language models (dMLLMs) is highlighted, with LLaDA-V and MMaDA being prominent examples that integrate visual and language processing capabilities [10][31]. - LLaDA-V combines visual instruction fine-tuning with the diffusion mechanism, demonstrating strong performance in multimodal understanding tasks [26][27]. - MMaDA showcases innovations in text reasoning and multimodal understanding, solidifying its position as a leading research outcome in the dMLLM space [31][32]. Group 4: Future Directions and Implications - The article emphasizes the shift from autoregressive models to diffusion models as a significant paradigm change in AI, suggesting broader implications for future research and applications [35][36]. - The ongoing evolution of models like LLaDA and Gemini Diffusion indicates a growing ecosystem around dLLMs and dMLLMs, with potential applications extending into quantum computing [35][36].

Core Viewpoint - The article introduces LaViDa, a large vision-language diffusion model that combines the advantages of diffusion models with the ability to process both visual and textual information effectively [1][5]. Group 1: Model Overview - LaViDa is a vision-language model that inherits the high speed and controllability of diffusion language models, achieving impressive performance in experiments [1][5]. - Unlike autoregressive large language models (LLMs), diffusion models treat text generation as a diffusion process over discrete tokens, allowing for better handling of tasks requiring bidirectional context [2][3][4]. Group 2: Technical Architecture - LaViDa consists of a visual encoder and a diffusion language model, connected through a multi-layer perceptron (MLP) projection network [10]. - The visual encoder processes multiple views of an input image, generating a total of 3645 embeddings, which are then reduced to 980 through average pooling for training efficiency [12][13]. Group 3: Training Methodology - The training process involves a two-stage approach: pre-training to align visual embeddings with the diffusion language model's latent space, followed by end-to-end fine-tuning for instruction adherence [19]. - A third training phase using distilled samples was conducted to enhance the reasoning capabilities of LaViDa, resulting in a model named LaViDa-Reason [25]. Group 4: Experimental Performance - LaViDa demonstrates competitive performance across various visual-language tasks, achieving the highest score of 43.3 on the MMMU benchmark and excelling in reasoning tasks [20][22]. - In scientific tasks, LaViDa scored 81.4 and 80.2 on ScienceQA, showcasing its strong capabilities in complex reasoning [23]. Group 5: Text Completion and Flexibility - LaViDa provides strong controllability for text generation, particularly in text completion tasks, allowing for flexible token replacement based on masked inputs [28][30]. - The model can dynamically adjust the number of tokens generated, successfully completing tasks that require specific constraints, unlike autoregressive models [31][32]. Group 6: Speed and Quality Trade-offs - LaViDa allows users to balance speed and quality by adjusting the number of diffusion steps, demonstrating flexibility in performance based on application needs [33][35]. - Performance evaluations indicate that LaViDa can outperform autoregressive baselines in speed and quality under certain configurations, highlighting its adaptability [35].