The paper introduces DiBS, a novel diffusion model-guided approach for branch selection in solving Sudoku, a constraint satisfaction problem. It enhances symbolic solvers by using a diffusion model to guide branch ordering, ensuring completeness while mitigating long-tail search issues.
This paper introduces the On-Policy Diffusion Language Model (OPDLM) for transforming autoregressive models (ARLMs) into diffusion language models (DLMs). It addresses issues like knowledge loss and train-inference mismatch by employing On-Policy Distillation (OPD).
The paper introduces "bicache", the first KV caching technique for shared prefixes in diffusion language models (DLMs), addressing challenges where existing LLM caching methods fail due to DLMs' bidirectional attention. This new approach aims to unlock high-throughput DLM serving by leveraging observations about shared prefix KVs stability in shallow layers.
DMax é um novo paradigma para modelos de linguagem de difusão (dLLMs) eficientes que mitiga o acúmulo de erros na decodificação paralela. Ele permite um paralelismo agressivo ao reformular a decodificação como um processo de auto-refinamento progressivo e introduzir uma estratégia de treinamento unificada.
The author built a diffusion language model from scratch to better understand complex concepts, without the help of AI-generated code. They trained the 7.5M parameter model on the tiny Shakespeare dataset and shared the code on GitHub.
Diffusion Large Language Models (dLLMs) face a "stability lag" due to irreversible token commitment, a problem exacerbated by Post-Training Quantization (PTQ) errors. FAIR-Calib proposes a two-stage PTQ framework that uses a position prior and layer-wise calibration to protect fragile frontier states, enhancing quantization for dLLMs.
The author explores the beauty and honesty of pausing diffusion models mid-render, finding these unfinished frames more revealing than polished final images. These stages, where AI models are still "thinking" and negotiating features from their training data, are described as a "confession" rather than a "statement."
This paper proposes a novel technique, Token-to-Mask (T2M) remasking, to refine masked diffusion language models like LLaDA2.1. The method addresses the shortcomings of Token-to-Token (T2T) editing by resetting suspect tokens to a mask state, enabling more accurate re-prediction.
Discrete Tilt Matching (DTM) is a novel likelihood-free method for fine-tuning masked diffusion large language models (dLLMs), addressing the intractability of sequence-level marginal likelihoods in RL. It recasts fine-tuning as state-level matching, using a weighted cross-entropy objective with control variates for stability, and achieves strong results on various tasks like Sudoku and Countdown.
This paper reveals a critical vulnerability in diffusion-based language models (dLLMs) where their safety alignment, based on monotonic denoising schedules, can be easily bypassed. By re-masking refusal tokens and injecting an affirmative prefix, researchers achieved high attack success rates against prominent dLLMs, exposing a structural flaw.
This paper provides a theoretical explanation for the efficiency of diffusion models in learning the score function for high-dimensional data supported on low-dimensional manifolds. It identifies a "collapse-and-refine" mechanism driven by the geometry of the score function, where the denoising map projects onto the data manifold and refines the intrinsic density.
OpenAI's new gpt-image-2 model has fundamentally solved the long-standing issue of AI models failing to accurately render text and complex layouts within images. This architectural pivot represents a significant advancement, necessitating a re-evaluation of workflows built around diffusion models.
This research unifies Transformers, diffusion-maps, and magnetic Laplacians, presenting them as different regimes of a single Markov geometry built from pre-softmax query-scores. It defines a QK "bidivergence" to connect attention and diffusion, organizing their dynamics with product of experts and Schrödinger-bridges.
Diffusion language models are now achieving significant throughput gains and narrowing the gap with autoregressive decoders in inference speed. New Introspective Diffusion Language Models (I-DLM) address prior issues of introspective consistency and inefficient sampling loops, improving both quality and latency.
This research systematically optimizes real-time diffusion model inference on Apple M3 Ultra, exploring various techniques like CoreML conversion, quantization, and model distillation. The study achieved 22.7 FPS for 512x512 img2img transformation by combining CoreML conversion of SDXS-512 with a 3-thread camera pipeline.
This article explores why AI image generators like Stable Diffusion and Midjourney consistently fail at rendering text correctly, explaining the issue stems from how diffusion models learn visual patterns. However, it hints at the existence of one model that has finally overcome this common limitation.
This paper investigates the limitations of uniform interventions in discrete diffusion language models (DLMs), demonstrating they degrade controlled generation quality. The authors find that different attributes commit at distinct stages of the denoising process, proposing an adaptive scheduler to concentrate interventions efficiently.
FLUID is a new framework designed to efficiently adapt Autoregressive (AR) backbones to the diffusion paradigm for parallel text generation. It enables initialization from GPT-style models and introduces an entropy-driven mechanism called Elastic Horizons, achieving state-of-the-art performance with significantly reduced training costs.
Este trabalho explora o agendamento de modelos para acelerar os Modelos de Linguagem de Difusão Mascarada (MDLMs), substituindo o modelo completo por um menor em certas etapas de denoising. A pesquisa mostra que as etapas iniciais e finais são mais robustas a essa substituição, permitindo uma redução de até 17% nos FLOPs com degradação mínima na perplexidade generativa.
This research introduces LIFT, a learnability-informed fine-tuning algorithm designed to enhance the reasoning capabilities of diffusion language models. LIFT addresses the shortcomings of standard SFT by adaptively learning tokens based on their difficulty and available context during different diffusion time steps, showing improved performance over existing baselines.