Zhaocong Yuan — accepted AI-conference papers

2025

OmniDraft: A cross-vocabulary, online adaptive drafter for on-device speculative decoding

Ramchalam Kinattinkara Ramakrishnan, Zhaocong Yuan, Shaojie Zhuo, Chen Feng, Yicheng Lin, Chenzheng Su +1

NeurIPS 2025poster

Speculative decoding generally dictates having a small, efficient draft model that is either pretrained or distilled offline to a particular target model series, for instance, Llama or Qwen models. However, within online deployment settings, there are two major challenges: 1) usage of a target model…

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2024

Stepping Forward on the Last Mile

Chen Feng, Shaojie Zhuo, Xiaopeng Zhang, Ramchalam Kinattinkara Ramakrishnan, Zhaocong Yuan, Andrew Zou Li

NeurIPS 2024poster

Continuously adapting pre-trained models to local data on resource constrained edge devices is the \emph{last mile} for model deployment. However, as models increase in size and depth, backpropagation requires a large amount of memory, which becomes prohibitive for edge devices. In addition, most ex…

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2022

Safe-Control-Gym: A Unified Benchmark Suite for Safe Learning-Based Control and Reinforcement Learning in Robotics

Zhaocong Yuan, Adam W. Hall, Siqi Zhou, Lukas Brunke, Melissa Greeff, Jacopo Panerati +1

RA-L 2022

In recent years, both reinforcement learning and learning-based control—as well as the study of their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">safety</i> , which is crucial for deployment in real-world robots—have gained significant traction.

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