3 citations · 3 across the 13 of their papers we have counts for
9 papers · 1 filter
Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models
Siyan Zhao, Zhihui Xie, Mengchen Liu +4
Knowledge distillation improves large language model (LLM) reasoning by compressing the knowledge of a teacher LLM to train smaller LLMs. On-policy distillation advances this appro…
Inpainting-Guided Policy Optimization for Diffusion Large Language Models
Siyan Zhao, Mengchen Liu, Jing Huang +8
Masked diffusion large language models (dLLMs) are emerging as promising alternatives to autoregressive LLMs, offering competitive performance while supporting unique generation ca…
Multi-fidelity Reinforcement Learning Control for Complex Dynamical Systems
Luning Sun, Xin-Yang Liu, Siyan Zhao +3
Controlling instabilities in complex dynamical systems is challenging in scientific and engineering applications. Deep reinforcement learning (DRL) has seen promising results for a…
Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs
Siyan Zhao, Mingyi Hong, Yang Liu +2
Large Language Models (LLMs) are increasingly used as chatbots, yet their ability to personalize responses to user preferences remains limited. We introduce PrefEval, a benchmark f…
DODT: Enhanced Online Decision Transformer Learning through Dreamer's Actor-Critic Trajectory Forecasting
Eric Hanchen Jiang, Zhi Zhang, Dinghuai Zhang +9
Advancements in reinforcement learning have led to the development of sophisticated models capable of learning complex decision-making tasks. However, efficiently integrating world…
Probing the Decision Boundaries of In-context Learning in Large Language Models
Siyan Zhao, Tung Nguyen, Aditya Grover
In-context learning is a key paradigm in large language models (LLMs) that enables them to generalize to new tasks and domains by simply prompting these models with a few exemplars…
Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models
Siyan Zhao, Daniel Israel, Guy Van den Broeck +1
During inference for transformer-based large language models (LLM), prefilling is the computation of the key-value (KV) cache for input tokens in the prompt prior to autoregressive…
Group Preference Optimization: Few-Shot Alignment of Large Language Models
Siyan Zhao, John Dang, Aditya Grover
Many applications of large language models (LLMs), ranging from chatbots to creative writing, require nuanced subjective judgments that can differ significantly across different gr…
Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models
Siyan Zhao, Aditya Grover
Reinforcement learning presents an attractive paradigm to reason about several distinct aspects of sequential decision making, such as specifying complex goals, planning future obs…