3 citations · 3 across the 13 of their papers we have counts for
17 papers
How Far Can Coding Agents Go in Visual Generation?
Hengguang Zhou, Yuanhao Ban, Jiaqi Feng +3
Modern image generation largely relies on specialized generative models that leave the process of image construction implicit in learned parameters. We explore an alternative: gene…
Post-Training Frontier Text-to-Image Models by Composing Preference and Rubric Rewards
Yuanhao Ban, I-Hung Hsu, Anastasios Angelopoulos +3
Recent text-to-image generation models have achieved remarkable visual quality, but improving them through post-training remains challenging because no single reward signal capture…
ReSPO: Reshaped Sequence Policy Optimization for Gradient Starvation in Off-Policy Learning
Yihang Chen, Yuanhao Ban, Cho-Jui Hsieh
Reinforcement learning from verifiable rewards (RLVR) frequently reuses rollouts across multiple policy updates, increasing the mismatch between the current policy and the data-gen…
SlackDrive: Reclaiming Runtime Slack for Adaptive Driving Inference
Xiaohuan Pei, Hengguang Zhou, Yuanhao Ban +7
Driving world-action models improve planning by coupling multimodal reasoning with future prediction, but their growing inference cost increasingly conflicts with the real-time lat…
ReCAST: Reward Credit Assignment across Timesteps for Online Diffusion Reinforcement
Yihang Chen, Yuanhao Ban, Kuei-Chun Kao +1
Training diffusion models with multiple rewards requires distinguishing user preference from reward informativeness. User preference determines how much each reward should contribu…
Stream4D: 4D-Consistency for Streaming Autoregressive Diffusion Video Models
Yuanhao Ban, Jiaqi Feng, Hengguang Zhou +3
Streaming autoregressive diffusion models enable real-time, long-horizon video generation, but their training objectives optimize local frame prediction rather than the geometry an…
Arena-T2I Hard: Benchmarking and Improving Faithfulness with Dependency-Aware Checklist
Yuanhao Ban, Tong Xie, Sohyun An +6
Faithfulness -- how precisely a generated image aligns with its prompt -- is increasingly central to the real-world utility of text-to-image (T2I) models. Existing faithfulness ben…
A Unifying Lens on Supervised Fine-Tuning Through Target Distribution Design
Tong Xie, Yuanhao Ban, Yunqi Hong +3
Supervised fine-tuning (SFT) typically maximizes the likelihood of every token in a demonstrated trajectory. However, an observed token can be non-unique, noisy, or misaligned with…
One-Forcing: Towards Stable One-Step Autoregressive Video Generation
Jiaqi Feng, Justin Cui, Yuanhao Ban +1
Recent advances in autoregressive diffusion-based video generation have substantially improved the quality of real-time video synthesis. However, most existing methods still requir…
AutoRubric-T2I: Robust Rule-Based Reward Model for Text-to-Image Alignment
Kuei-Chun Kao, Daixuan Huo, Yuanhao Ban +1
Aligning Text-to-Image (T2I) generation models with human preferences increasingly relies on image reward models that score or rank generated images according to prompt alignment a…
LoL: Longer than Longer, Scaling Video Generation to Hour
Justin Cui, Jie Wu, Ming Li +6
Recent research in long-form video generation has shifted from bidirectional to autoregressive models, yet these methods commonly suffer from error accumulation and a loss of long-…
Reward-Forcing: Autoregressive Video Generation with Reward Feedback
Jingran Zhang, Ning Li, Yuanhao Ban +2
While most prior work in video generation relies on bidirectional architectures, recent efforts have sought to adapt these models into autoregressive variants to support near real-…
When Distance Distracts: Representation Distance Bias in BT-Loss for Reward Models
Tong Xie, Andrew Bai, Yuanhao Ban +3
Reward models are central to Large Language Model (LLM) alignment within the framework of RLHF. The standard objective used in reward modeling is the Bradley-Terry (BT) loss, which…
Self-Forcing++: Towards Minute-Scale High-Quality Video Generation
Justin Cui, Jie Wu, Ming Li +6
Diffusion models have revolutionized image and video generation, achieving unprecedented visual quality. However, their reliance on transformer architectures incurs prohibitively h…
IRIS: Intrinsic Reward Image Synthesis
Yihang Chen, Yuanhao Ban, Yunqi Hong +1
Despite the success of Reinforcement Learning from Human Feedback (RLHF) in language reasoning, its application to autoregressive Text-to-Image (T2I) generation is often constraine…
Understanding the Impact of Negative Prompts: When and How Do They Take Effect?
Yuanhao Ban, Ruochen Wang, Tianyi Zhou +3
The concept of negative prompts, emerging from conditional generation models like Stable Diffusion, allows users to specify what to exclude from the generated images.%, demonstrati…
The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise
Yuanhao Ban, Ruochen Wang, Tianyi Zhou +3
Diffusion models have achieved remarkable success in text-to-image generation tasks; however, the role of initial noise has been rarely explored. In this study, we identify specifi…