2 citations · 7 across the 12 of their papers we have counts for
13 papers
OPD+: Rethinking the Advantage Design for On-Policy Distillation
Hanyang Zhao, Haoxian Chen, Han Lin +3
On-policy distillation (OPD) is a widely used technique to transfer capabilities from capable teacher language models to the base student models, and can be formulated in a reinfor…
SOCRATES: Simulation Optimization with Correlated Replicas and Adaptive Trajectory Evaluations
Haoting Zhang, Haoxian Chen, Donglin Zhan +5
The field of simulation optimization (SO) encompasses various methods developed to optimize complex, expensive-to-sample stochastic systems. Established methods include, but are no…
Understanding Sampler Stochasticity in Training Diffusion Models for RLHF
Jiayuan Sheng, Hanyang Zhao, Haoxian Chen +2
Reinforcement Learning from Human Feedback (RLHF) is increasingly used to fine-tune diffusion models, but a key challenge arises from the mismatch between stochastic samplers used…
DiFFPO: Training Diffusion LLMs to Reason Fast and Furious via Reinforcement Learning
Hanyang Zhao, Dawen Liang, Wenpin Tang +2
We propose DiFFPO, Diffusion Fast and Furious Policy Optimization, a unified framework for training masked diffusion large language models (dLLMs) to reason not only better (furiou…
RPO: Fine-Tuning Visual Generative Models via Rich Vision-Language Preferences
Hanyang Zhao, Haoxian Chen, Yucheng Guo +5
Traditional preference tuning methods for LLMs/Visual Generative Models often rely solely on reward model labeling, which can be opaque, offer limited insights into the rationale b…
Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning
Hanyang Zhao, Haoxian Chen, Ji Zhang +2
Reinforcement learning from human feedback (RLHF), which aligns a diffusion model with input prompt, has become a crucial step in building reliable generative AI models. Most works…
WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines
Genta Indra Winata, Frederikus Hudi, Patrick Amadeus Irawan +48
Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly in languages other than English and in underrepresented cultural contexts. To evaluate th…
RainbowPO: A Unified Framework for Combining Improvements in Preference Optimization
Hanyang Zhao, Genta Indra Winata, Anirban Das +4
Recently, numerous preference optimization algorithms have been introduced as extensions to the Direct Preference Optimization (DPO) family. While these methods have successfully a…
Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning
Hanyang Zhao, Haoxian Chen, Ji Zhang +2
Reinforcement Learning from human feedback (RLHF) has been shown a promising direction for aligning generative models with human intent and has also been explored in recent works f…
Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey
Genta Indra Winata, Hanyang Zhao, Anirban Das +4
Preference tuning is a crucial process for aligning deep generative models with human preferences. This survey offers a thorough overview of recent advancements in preference tunin…
MallowsPO: Fine-Tune Your LLM with Preference Dispersions
Haoxian Chen, Hanyang Zhao, Henry Lam +2
Direct Preference Optimization (DPO) has recently emerged as a popular approach to improve reinforcement learning with human feedback (RLHF), leading to better techniques to fine-t…
Score-based Diffusion Models via Stochastic Differential Equations -- a Technical Tutorial
Wenpin Tang, Hanyang Zhao
This is an expository article on the score-based diffusion models, with a particular focus on the formulation via stochastic differential equations (SDE). After a gentle introducti…
Contractive Diffusion Probabilistic Models
Wenpin Tang, Hanyang Zhao
Diffusion probabilistic models (DPMs) have emerged as a promising technique in generative modeling. The success of DPMs relies on two ingredients: time reversal of diffusion proces…