6 papers · 1 filter
Score Mismatch and Set-Constrained Sampling in Zero-Shot Conditional Discrete Diffusion
Renxiang Huang, Yuchen Liang, Yingbin Liang +2
Discrete diffusion models have become a powerful framework for modeling complex discrete distributions across a range of applications. While theoretical convergence guarantees depe…
Understanding Unified VLMs: From Attention Probing to Guidance Tuning for Multimodal Generation
Zhengyang Luo, Xiaoyu Qiu, Zhiheng Tan +3
Unified vision-language models (VLMs) perform both text-to-image (T2I) and image-to-text (I2T) generation with one shared Transformer backbone, yet how the backbone processes the c…
PACT: Privileged Trace Co-Training for Multi-Turn Tool-Use Agents
Zhenbang Du, Jun Luo, Zhiwei Zheng +8
Multi-turn tool-use agents must reason, call tools, and adapt to observations across several interaction turns. Post-training such agents is challenging, as reinforcement learning…
From Scores to Gibbs Correctors: Accelerating Uniform-Rate Discrete Diffusion Models
Yuchen Liang, Ness Shroff, Yingbin Liang
Discrete diffusion models have achieved strong empirical performance in text and other symbolic domains, but, especially for uniform-rate models, they often require many steps to g…
Regret Bounds for Reinforcement Learning from Multi-Source Imperfect Preferences
Ming Shi, Yingbin Liang, Ness B. Shroff +1
Reinforcement learning from human feedback (RLHF) replaces hard-to-specify rewards with pairwise trajectory preferences, yet regret-oriented theory often assumes that preference la…
Learnable Chernoff Baselines for Inference-Time Alignment
Sunil Madhow, Yuchen Liang, Ness Shroff +2
We study inference-time reward-guided alignment for generative models. Existing methods often rely on either architecture-specific adaptations or computationally costly inference p…