Yuchen Zhu

Scaling Reinforcement Learning for Diffusion Models via Velocity Matching

Jaemoo Choi, Wei Guo, Yuchen Zhu +4

Reward-based fine-tuning of diffusion models has largely inherited likelihood-based policy optimization developed for autoregressive large language models (LLMs). Diffusion models,…

Thinking with Anchors: Grounded and Efficient Document Reasoning

Sichen Zhu, Yuchen Zhu, Wenzhuo Xu +13

Existing document understanding benchmarks have largely focused on locating page elements, yet real-world document intelligence requires models to reason jointly about region seman…

A2D2: Fine-Tuning Any-Length Discrete Diffusion for Adaptive Decoding

Sophia Tang, Yuchen Zhu, Molei Tao +1

Discrete diffusion models offer a simple and stable likelihood-based framework for sequence generation, recently extended to any-length settings via token insertion. Principled rew…

cs.AI2026

Agents' Last Exam

Yiyou Sun, Xinyang Han, Weichen Zhang +378

Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…

FLARE: Diffusion for Hybrid Language Model

Yuchen Zhu, Jing Shi, Chongjian Ge +9

Autoregressive (AR) large language models (LLMs) have achieved broad practical success, but sequential decoding remains a key bottleneck for low-latency deployment. Recent efficien…

Efficient Adjoint Matching for Fine-tuning Diffusion Models

Jeongwoo Shin, Dongsoo Shin, Yuchen Zhu +5

Reward fine-tuning has become a common approach for aligning pretrained diffusion and flow models with human preferences in text-to-image generation. Among reward-gradient-based me…

ACWM-Phys: Investigating Generalized Physical Interaction in Action-Conditioned Video World Models

Haotian Xue, Yipu Chen, Liqian Ma +4

Action-conditioned world models (ACWMs) have shown strong promise for video prediction and decision-making. However, existing benchmarks are largely restricted to egocentric naviga…

stat.ML2026

Discrete Adjoint Schrödinger Bridge Sampler

Wei Guo, Yuchen Zhu, Xiaochen Du +6

Learning discrete neural samplers is challenging due to the lack of gradients and combinatorial complexity. While stochastic optimal control (SOC) and Schrödinger bridge (SB) provi…

LaViDa-R1: Advancing Reasoning for Unified Multimodal Diffusion Language Models

Shufan Li, Yuchen Zhu, Jiuxiang Gu +6

Diffusion language models (dLLMs) recently emerged as a promising alternative to auto-regressive LLMs. The latest works further extended it to multimodal understanding and generati…

Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design

Jaemoo Choi, Yuchen Zhu, Wei Guo +6

Reinforcement learning has been widely applied to diffusion and flow models for visual tasks such as text-to-image generation. However, these tasks remain challenging because diffu…

Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models

Jinbin Bai, Yixuan Li, Yuchen Zhu +8

Inference-time compute has re-emerged as a practical way to improve LLM reasoning. Most test-time scaling (TTS) algorithms rely on autoregressive decoding, which is ill-suited to d…

From Masks to Worlds: A Hitchhiker's Guide to World Models

Jinbin Bai, Yu Lei, Hecong Wu +7

This is not a typical survey of world models; it is a guide for those who want to build worlds. We do not aim to catalog every paper that has ever mentioned a ``world model". Inste…

Enhancing Reasoning for Diffusion LLMs via Distribution Matching Policy Optimization

Yuchen Zhu, Wei Guo, Jaemoo Choi +4

Diffusion large language models (dLLMs) are promising alternatives to autoregressive large language models (AR-LLMs), as they potentially allow higher inference throughput. Reinfor…

Proximal Diffusion Neural Sampler

Wei Guo, Jaemoo Choi, Yuchen Zhu +2

The task of learning a diffusion-based neural sampler for drawing samples from an unnormalized target distribution can be viewed as a stochastic optimal control problem on path mea…

TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion

Sophia Tang, Yuchen Zhu, Molei Tao +1

Reinforcement learning with stochastic optimal control offers a promising framework for diffusion fine-tuning, where a pre-trained diffusion model is optimized to generate paths th…

MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control

Yuchen Zhu, Wei Guo, Jaemoo Choi +3

We study the problem of learning a neural sampler to generate samples from discrete state spaces where the target probability mass function $π \propto e^{- U}$ is known up to…

Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces

Kevin Rojas, Yuchen Zhu, Sichen Zhu +2

Diffusion models have demonstrated remarkable performance in generating unimodal data across various tasks, including image, video, and text generation. On the contrary, the joint…

cs.CV2025

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models

Chengyue Huang, Yuchen Zhu, Sichen Zhu +4

Vision-language models (VLMs) are widely assumed to exhibit in-context learning (ICL), a property similar to that of their language-only counterparts. While recent work suggests VL…