Bo Liu - Recursive Superintelligence

I am a Member of Technical Staff at Recursive Superintelligence, working on pretraining and continual learning. My research interest lies in designing theoretically sound algorithms and neural architectures for agents with persistent memory and continual improvements. I received my Ph.D. from UT Austin, where I was fortunately co-advised by Prof. Peter Stone and Prof. Qiang Liu. Prior to that I completed my master degree at Stanford and my undergraduate degree at JHU.

Email / Github / Google Scholar / X / Thesis

Experience

Aug 2026 - Present

Recursive Superintelligence – Member of Technical Staff (RSI)
Pretraining, Continual Learning

Nov 2025 - Aug 2026

Microsoft, Mountain View – Member of Technical Staff (MSI)
Continual Learning; Manager: Weizhu Chen

Nov 2024 - Nov 2025

Meta, Menlo Park – Senior Research Scientist (MSL)
Reasoning, Scaling RL; Manager: Yuandong Tian

Jun - Oct 2023

DeepMind, London – Research Intern
Mentor: Arthur Szlam, Marc'aurelio Ranzato

Jun - Sept 2020

Nvidia, Sunnyvale – Research Intern
Mentor: Animashree Anandkumar, Yuke Zhu

Jun - Sept 2018/2019

Baidu, Sunnyvale – Research Intern
Mentor: Ping Li

May - Aug 2015/2016

Google, Los Angeles – Software Engineer Intern

Selected Publications (* ⇒ equal contribution)

Maglev: Sliding Recurrent Memory

Bo Liu, Qiang Liu

paper

Longhorn: State Space Models Are Amortized Online Learners

Bo Liu, Rui Wang, Lemeng Wu, Yihao Feng, Peter Stone, Qiang Liu

Oral presentation at ENLSP@NeurIPS 2024

paper / code

AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies

Xixi Hu, Bo Liu, Xingchao Liu, Qiang Liu

NeurIPS 2024

paper / code

Memory-Efficient LLM Training with Online Subspace Descent

Kaizhao Liang, Bo Liu, Lizhang Chen, Qiang Liu

NeurIPS 2024

paper / code

Communication Efficient Distributed Training with Distributed Lion

*Bo Liu, *Lemeng Wu, *Lizhang Chen, Kaizhao Liang, Jiaxu Zhu, Chen Liang, Raghuraman Krishnamoorthi, Qiang Liu

NeurIPS 2024

paper

Asynchronous Local-SGD Training for Language Modeling

Bo Liu, Rachita Chhaparia, Arthur Douillard, Satyen Kale, Andrei A. Rusu, Jiajun Shen, Arthur Szlam, Marc'Aurelio Ranzato

Oral presentation at WANT@ICML 2024

paper / code

Lion Secretly Solves Constrained Optimization: As Lyapunov Predicts

*Lizhang Chen, *Bo Liu, *Kaizhao Liang, Qiang Liu

ICLR 2024 (Spotlight)

paper

LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

*Bo Liu, *Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, Peter Stone

paper / code

LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

*Bo Liu, *Yifeng Zhu, *Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, Peter Stone

NeurIPS 2023 (oral presentation at RAP4Robots, ICRA 2023, and TGR, CoRL 2023)

paper / code / website

FAMO: Fast Adaptive Multitask Optimization

Bo Liu, Yihao Feng, Peter Stone, Qiang Liu

NeurIPS 2023

paper / code

Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement Learning

Bo Liu, Yihao Feng, Qiang Liu, Peter Stone

AAAI 2023 (oral presentation)

paper / code

BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach

*Bo Liu, *Mao Ye, Peter Stone, Steve Wright, Qiang Liu

NeurIPS 2022

paper / code

Continual Learning and Private Unlearning

Bo Liu, Qiang Liu, Peter Stone

CoLLAs 2022

paper / code

Conflict-Averse Gradient Descent for Multi-task Learning

Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu

NeurIPS 2021

paper / code

Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition

Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Animashree Anandkumar

ICML 2021 (oral presentation)

paper / code

A lifelong learning approach to mobile robot navigation

*Bo Liu, *Xuesu Xiao, Peter Stone

RA-L / ICRA 2021

paper / code

Firefly neural architecture descent: a general approach for growing neural networks

*Lemeng Wu, *Bo Liu, Peter Stone, Qiang Liu

NeurIPS 2020

paper / code

Teaching

Fall 2019	University of Texas at Austin – Teaching Assistant CS 394: Reinforcement Learning
Winter 2019	Stanford University – Teaching Assistant CS 234: Reinforcement Learning
Fall 2016	Johns Hopkins University – Teaching Assistant EN 601.665: Natural Language Processing
Fall 2015	Johns Hopkins University – Teaching Assistant EN 600.463: Algorithms