New research advances diffusion models for quantization, motion generation, and RLHF
ByPulseAugur Editorial·[33 sources]·
Researchers have developed new methods for improving diffusion models. One paper introduces Guidance-Aware Mixed Precision (GAMP) to address quantization challenges in classifier-free guidance (CFG) diffusion models, preventing unconditional branch drift. Another paper presents ARDY, a framework for real-time, high-fidelity 3D human motion generation with text and kinematic control. Additionally, a new approach called ContrastiveCFG is proposed to enhance conditional diffusion model sampling by using contrastive loss for better concept alignment and filtering. Finally, advancements in sample-efficient diffusion RLHF are detailed, featuring selective timestep weighting and advantage-based replay to improve feedback efficiency.
AI
IMPACT
These advancements could lead to more efficient and capable diffusion models for various applications, from content generation to robotics.
RANK_REASON
Multiple research papers published on arXiv detailing advancements in diffusion models, quantization, motion generation, and RLHF.
arXiv:2607.08741v1 Announce Type: cross Abstract: Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics. While recent offline motion generation approaches offer precise control via text and kinem…
arXiv cs.LG
TIER_1English(EN)·Abdullah Al Shafi, Sumaiya Rahim Suma·
Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics. While recent offline motion generation approaches offer precise control via text and kinematic constraints, they lack the inference speed re…
Diffusion unlearning is essential for mitigating the generation of harmful or copyrighted content in text-to-image models. Current diffusion unlearning techniques determine the model update direction by either using alternatives of the target concept as an anchor or using empty p…
arXiv:2607.07330v1 Announce Type: cross Abstract: Hypergraph neural networks have shown powerful capability in modeling higher-order relations, yet their predictive uncertainty remains underexplored. Unlike pairwise graphs, uncertainty in hypergraphs arises not only from noisy at…
arXiv:2607.07693v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) has emerged as a powerful paradigm for aligning generative models with human preferences. However, applying RLHF to diffusion models remains highly feedback inefficient, as existin…
arXiv:2411.17077v2 Announce Type: replace-cross Abstract: As Classifier-Free Guidance (CFG) has proven effective in conditional diffusion model sampling for improved condition alignment, many applications use a negated CFG term as a Negative Prompting (NP) to filter out unwanted …
arXiv:2607.06833v1 Announce Type: new Abstract: Sampling stochastic signals supported on a graph underlies many graph machine learning tasks, including recommender systems, forecasting in financial markets, and wireless network optimization. In these settings, the target signals …
ARDY is a streaming generation framework that enables real-time, high-fidelity 3D human motion generation with text and kinematic constraint control through a hybrid representation and two-stage autoregressive transformer denoiser.
Reinforcement learning from human feedback (RLHF) has emerged as a powerful paradigm for aligning generative models with human preferences. However, applying RLHF to diffusion models remains highly feedback inefficient, as existing approaches typically require large amounts of hu…
Hypergraph neural networks have shown powerful capability in modeling higher-order relations, yet their predictive uncertainty remains underexplored. Unlike pairwise graphs, uncertainty in hypergraphs arises not only from noisy attributes and ambiguous labels, but also from varia…
arXiv:2607.05711v1 Announce Type: new Abstract: Diffusion models have become a dominant paradigm for high-quality generative modeling, while post-training is essential for adapting them to diverse downstream applications. However, post-training of large diffusion models is still …
arXiv:2408.00001v2 Announce Type: replace-cross Abstract: Visual diffusion models have revolutionized the field of creative AI, producing high-quality and diverse content. However, they inevitably memorize training images or videos, subsequently replicating their concepts, conten…
arXiv cs.LG
TIER_1English(EN)·Amandeep Kumar, Vishal M. Patel·
arXiv:2602.10099v2 Announce Type: replace Abstract: Leveraging representation encoders for generative modeling offers a path for efficient, high-fidelity synthesis. However, standard diffusion transformers fail to converge on these representations directly. While recent work attr…
arXiv cs.AI
TIER_1English(EN)·Rajat Rasal, Avinash Kori, Tian Xia, Ben Glocker·
arXiv:2607.05319v1 Announce Type: cross Abstract: We study why diffusion autoencoders can achieve similar image quality while learning substantially different latent structures. We trace this behaviour to optimisation dynamics; we analyse curves of image reconstruction against la…
Diffusion models have become a dominant paradigm for high-quality generative modeling, while post-training is essential for adapting them to diverse downstream applications. However, post-training of large diffusion models is still challenging due to the prohibitive memory footpr…
We study why diffusion autoencoders can achieve similar image quality while learning substantially different latent structures. We trace this behaviour to optimisation dynamics; we analyse curves of image reconstruction against latent representation quality, revealing trajectorie…
Generative reasoning re-rankers achieve strong recommendation accuracy by emitting a chain-of-thought before re-ordering a candidate list, but they are slow at inference: an autoregressive (AR) decoder spends one sequential forward pass per reasoning token, and the reasoning trac…
Generative reasoning re-rankers achieve strong recommendation accuracy by emitting a chain-of-thought before re-ordering a candidate list, but they are slow at inference: an autoregressive (AR) decoder spends one sequential forward pass per reasoning token, and the reasoning trac…
arXiv:2607.08056v1 Announce Type: cross Abstract: Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks. Recent research demonstrates that adaptive token generation ordering can significantly improve performance in mathe…
arXiv:2607.08337v1 Announce Type: cross Abstract: Diffusion unlearning is essential for mitigating the generation of harmful or copyrighted content in text-to-image models. Current diffusion unlearning techniques determine the model update direction by either using alternatives o…
Diffusion unlearning is essential for mitigating the generation of harmful or copyrighted content in text-to-image models. Current diffusion unlearning techniques determine the model update direction by either using alternatives of the target concept as an anchor or using empty p…
arXiv stat.ML
TIER_1English(EN)·Robert Gruhlke, Julius Berner, David Sommer, Lorenz Richter·
arXiv:2607.06841v1 Announce Type: new Abstract: Diffusion models offer a powerful framework for sampling from complex probability densities by learning to reverse a noising process. A common approach involves solving for the time-reversed stochastic differential equation (SDE), w…
Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks. Recent research demonstrates that adaptive token generation ordering can significantly improve performance in mathematical reasoning and code synthesis applications.…
arXiv:2607.06136v1 Announce Type: new Abstract: Recent diffusion-based generative models have shown impressive performance in image generation and editing. However, due to memory limitations and the high cost of collecting high-resolution training images, existing methods are typ…
Diffusion models offer a powerful framework for sampling from complex probability densities by learning to reverse a noising process. A common approach involves solving for the time-reversed stochastic differential equation (SDE), which requires the score function of the evolving…
Recent diffusion-based generative models have shown impressive performance in image generation and editing. However, due to memory limitations and the high cost of collecting high-resolution training images, existing methods are typically restricted to inputs with linear resoluti…
arXiv:2607.03899v1 Announce Type: new Abstract: Diffusion models have become a dominant paradigm for conditional image generation, yet existing approaches generally follow two directions: task-specific designs that can improve performance but limit generalization, and training-fr…
arXiv:2603.06577v2 Announce Type: replace Abstract: While recent multimodal large language models (MLLMs) have made impressive strides, they predominantly employ a conventional autoregressive architecture as their backbone, leaving significant room to explore effective and effici…
arXiv:2606.20971v2 Announce Type: replace Abstract: We introduce UNITY, a Universal-to-Specialized adapter for efficient and scalable composite conditioning in diffusion based image generation. Unlike prior methods that train separate adapters for each conditioning modality, UNIT…
<!-- SC_OFF --><div class="md"><p>Single-stream diffusion transformer with a DeepSeek-V3-style sparse MoE (128 experts, top-8 routing, 1.4B active of 13B total). Six-reward RL post-training including a physical-plausibility reward, plus an action-to-video mode that predicts robot…
