Siliang Zeng
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About me
I am a research scientist at Bytedance Seed. Previously, I obtained my PhD at University of Minnesota, advised by Prof. Mingyi Hong.
Before moving to Minnesota, I received the B.S. in Statistics from The Chinese University of Hong Kong, Shenzhen in 2020.
In my research, I am always thinking about how to design practical algorithms and systems for training RL agents to solve sequential decision-making
problems under uncertainty. My recent research interests focus on Foundation Model Alignment and Agent.
Experience
Research Scientist, Bytedance Seed, San Jose, CA, Jun 2025 - Present.
Research Intern, Morgan Stanley ML Research, New York, NY, Feb 2025 - Apr 2025.
Conducted Research in Agentic RL and Reasoning Models. (Mentor: Will Brown)
Applied Scientist Intern, Amazon Web Search, Santa Clara, May 2024 - Oct 2024.
Design RLHF algorithm to align LLMs with high-quality demonstrations. (Mentor: Yao Liu and Rasool Fakoor)
Applied Scientist Intern, Amazon Web Search, Santa Clara, May 2023 - Sep 2023.
Design RLHF algorithm for LLM alignment with low compuational cost. (Mentor: Kaixiang Lin)
Recent News
Jun 2025: One paper is accepted to ICML 2025 Workshop on Computer Use Agents:
Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Credit Assignment.
May 2025: I passed my PhD defense at University of Minnesota!
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Mar 2025: One paper is accepted to Reasoning and Planning for LLMs workshop @ ICLR2025:
Reinforcement Learning in Inference Time: A Perspective from Successive Policy Iterations.
Feb 2025: One paper is accepted to ICLR 2025 as a spotlight:
Joint Demonstration and Preference Learning Improves Policy Alignment with Human Feedback.
Jan 2025: One US Patent with IBM researchers has been granted:
BILEVEL DECENTRALIZED MULTI-AGENT LEARNING
Jan 2025: One paper has been accepted to TMLR:
Bridging the Training-Inference Gap in LLMs by Leveraging Self-Generated Tokens
Jan 2025: Two papers have been accepted to ICLR 2025 and AISTATS 2025:
Joint Demonstration and Preference Learning Improves Policy Alignment with Human Feedback.
Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimality.
Oct 2024: Two papers got accepted to NeurIPS 2024 Workshop on Fine-Tuning in Modern Machine Learning: Principles and Scalability (FITML 2024):
Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment.
Policy Optimization can be Memory-Efficient: LLM Alignment Through Successive Policy Re-weighting (SPR).
Sep 2024: One paper has been accepted to NeurIPS 2024:
Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment
August 2024: Our paper titled “A stochastic linearized augmented Lagrangian method for decentralized bilevel optimization” has been
selected as an Honorable Mention in the IBM Pat Goldberg Memorial competition for best papers!
July 2024: One paper has been accepted to Operations Research:
Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees
Jun 2024: One paper has been accepted to ICML 2024 Workshop on Theoretical Foundations of Foundation Models:
Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment
May 2024: I join Amazon Web Search (AWS) as an applied scientist intern. I will work on large language model alignment.
Apr 2024: I am thrilled to receive the 2024-25 Doctoral Dissertation Fellowship (DDF).
Apr 2024: I am invited to give a talk at Ai4 - Industry's Leading AI Conference. See you in Las Vegas!
Nov 2023: I am invited to give a talk at the Coordinated Science Laboratory of UIUC.
Oct 2023: I am thrilled to receive the NeurIPS 2023 Scholar Award. See you in New Orleans!
Sep 2023: One paper has been accepted to NeurIPS 2023 as an oral:
When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning.
August 2023: One paper has been accepted to Conference on Robot Learning (CoRL) 2023:
A Bayesian Approach to Robust Inverse Reinforcement Learning.
May 2023: I join Amazon Web Search (AWS) as an applied scientist intern. I will work on reinforcement learning and large language model training.
March 2023: I gave an invited talk at Amazon AWS about our recent work on offline inverse reinforcement learning:
Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
Oct 2022: I am thrilled to receive the NeurIPS 2022 Scholar Award. See you in New Orleans!
Sep 2022: Two papers have been accepted to NeurIPS 2022:
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees.
A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization.
Jul 2022: I have been selected for a travel grant from the DARL Workshop at ICML 2022!
Jun 2022: One paper has been accepted to Decision Awareness in Reinforcement Learning Workshop at ICML 2022:
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees.
Jun 2022: One paper has been accepted to SIAM Journal on Optimization:
On the Divergence of Decentralized Non-Convex Optimization.
Mar 2022: One paper has been accepted to L4DC 2022:
Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time Guarantees.
Sep 2021: One paper has been accepted to NeurIPS 2021:
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum.