The paper introduces Best‑of‑N Guidance (BoNG), a method that embeds the Best‑of‑N sampling principle directly into the reverse diffusion process of generative models. By performing online BoN selection over denoising particles and using the current best particle as a guidance signal, BoNG steers the particle population toward higher‑reward regions during generation. Empirical results show that BoNG outperforms both standard BoN and SMC across 36 comparisons, achieving the best performance in 29 cases and supporting multi‑output generation with significant speedup.
By Richard Lee Kim, Yeongmin Kim, Gyuwon Sim, Taekyu Kim, Minsang Park, Il-chul Moon
The paper introduces “Spend Where It Matters”, a two‑stage, training‑free framework that combines particle search with Metropolis‑Hastings refinement for budget‑constrained audio‑visual alignment. In the first stage, particles are adaptively allocated across denoising checkpoints based on online variance estimates, reducing estimation variance while focusing compute where it matters most. The second stage uses MH refinement with local noise‑and‑denoise proposals to further improve reward, starting from the strong candidate produced by the particle search. Experiments on “JavisBench‑Mini” show that this combined approach yields higher rewards than either particle search or MH alone, achieving significant relative gains over baseline methods. The method also demonstrates benefits for image generation on SD1.5, improving aesthetic and image reward scores compared to uniform particle allocation.
By Shivanshu Shekhar, Raghavendra Addanki, Tong Zhang, Uttaran Bhattacharya
arXiv:2610.17266v1 Announce Type: new
Abstract: Diffusion language models are a promising and more efficient alternative to autoregressive models. However, most RL post-training techniques are not ea...
By Vaibhav Singh, Eugene Belilovsky
arXiv:2610.00544v1 Announce Type: new
Abstract: Streaming world models should render a place consistently across repeated visits. Directly supervising such revisits requires training samples that cap...
By Tingting Liao, Xuezhi Liang, Hao Li, Guangyi Liu
Speculative Forcing is a framework for efficient autoregressive video generation that treats intermediate clean estimates as drafts and predicts when individual tokens can be safely accepted early. Tokens that are accepted are frozen and no longer undergo further denoising, while their cached keys and values remain available for context. Two verifier designs—one training‑free based on similarity and stability, and one learned that uses intermediate features—enable this selective refinement, preserving full‑step quality while reducing computation across short‑video, long‑video, and world generation tasks.
By Hyunwook Choi, Seungryong Kim, Dongyoon Han, Sangdoo Yun
arXiv:2609.39096v1 Announce Type: new
Abstract: Autoregressive video diffusion supports streaming generation and interactive control, but its KV cache grows with the generated history. Existing compr...
By Zeqi Xiao, Qingle Liu, Kaiwen Zhang, Yifan Zhou, Zihan Ding, Xingang Pan
The paper introduces TRACK, a training‑free trajectory routing method that accelerates video diffusion by selectively switching between large and small models during denoising steps. A calibration process generates a disagreement score map, guiding the selection of the appropriate model at each step to maintain quality while reducing computational cost. Experiments on Wan 2.1, Cosmos 3, TurboDiffusion, and FastVideo show speedups ranging from 1.95× to 2.73× with comparable quality and diversity.
By Mustafa Munir, Huy Vu, Shreyas Misra, Rohit Jena, Sajad Norouzi, Ali Taghibakhshi, Anis Ahmad, Anjul Patney, Pavlo Molchanov, Nima Tajbakhsh
The paper introduces a framework that unifies diffusion and autoregressive (AR) generative models as paths on a corruption lattice, defining a cost for decoding schedules based on discarded parallel steps. It shows that the minimal number of steps for a zero‑cost schedule is determined by the data’s geometry—specifically, the graph’s treedepth for Markov data—providing a theoretical limit for both token and continuous field models. By estimating pairwise dependencies from pretrained weights, the authors predict the ranking of decoding schedules before execution, and validate these predictions across text, image, and video generation tasks.
By T. Y. Tsui, Jiatao Gu, Lingjie Liu
Accelerating Video Diffusion via Training-Free Trajectory Routing (TRACK) introduces a heterogeneous denoising strategy that switches between large and small diffusion models at selected steps, determined by a calibration process that measures disagreement between model predictions. By routing quality-sensitive steps to the large model and low-disagreement steps to the small model, TRACK achieves significant speedups—up to 2.73×—across several video diffusion benchmarks while maintaining comparable quality and diversity. The method requires no retraining, architectural changes, or online dual-model evaluation, making it a practical acceleration paradigm for video diffusion.
The paper introduces ZENDAYA, a closed‑form bandwidth dial that replaces fixed wait‑k rules in streaming multimodal decoders with a single continuous parameter γ. This schedule ties the visible source prefix to generation progress and the input’s predicted length, allowing a single offline decoder to serve as both offline and real‑time streaming models. Experiments on video‑to‑text and audio‑to‑text tasks show that reading less of the source can improve text quality, with significant METEOR gains across three public corpora.
By Yasir Mehmood, Kashif Javed
The paper introduces GRAS, a method that improves training‑free reward alignment for discrete diffusion models by reducing variance in guided proposals and adapting the resampling temperature during search. It achieves this without adding denoiser cost, using Rao‑Blackwellized estimates for differentiable rewards and a leave‑one‑out baseline for non‑differentiable ones. Experiments on regulatory DNA and protein design show GRAS outperforms existing training‑free techniques and rivals reward‑fine‑tuned models.
By Kwanyoung Kim
arXiv:2608. 03929v1 Announce Type: new Abstract: Aligning diffusion models with human preferences usually relies on a sparse terminal reward evaluated on the final generated samples, presenting a severe temporal credit-assignment challenge across the multi-step denoising process.
By Yuanshen Guan, Zipeng Feng, Zhiwei Xiong, Peiqin Sun