2 citations · 3 across the 14 of their papers we have counts for
5 papers · 1 filter
JustOPD: On-Policy Distillation from Autoregressive to Diffusion Language Models
Zikun Zhang, Jiayuan Sheng, David D. Yao +1
On-policy distillation (OPD) is a post-training approach for large language models (LLMs) that enables a student model to learn a teacher model's distribution from sequences sample…
Spec2Twin-Chain: Orchestrating Bi-Level Optimization with LLMs for Blockchain Digital Twin Construction
Haoting Zhang, Haoxian Chen, Jiayuan Sheng +4
Building a blockchain digital twin largely requires translating domain knowledge and specific system descriptions into a simulator architecture, calibrating its parameters against…
A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models
Zikun Zhang, Jiayuan Sheng, David D. Yao +1
We formulate reinforcement learning (RL) in continuous time with discrete state spaces and possibly arbitrary action spaces via a stochastic control approach, where the state dynam…
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…
Improved techniques for fine-tuning flow models via adjoint matching: a deterministic control pipeline
Zhengyi Guo, Jiayuan Sheng, David D. Yao +1
We propose a deterministic adjoint matching framework that formulates human preference alignment for flow-based generative models as an optimal control problem over velocity fields…