Chris Lu

I'm currently a researcher at OpenAI. My most well-known works are on The AI Scientist, and the PureJaxRL ecosystem.

Previously, I was a DPhil student at the University of Oxford, where I was advised by Professor Jakob Foerster at FLAIR. My work focused on automating open-ended discovery and multi-agent systems. I previously interned at Sakana.ai and at DeepMind as a research scientist. Before that, I worked as a researcher at Covariant.ai after graduating from UC Berkeley.

Google Scholar / Twitter / Github / LinkedIn

Updates

(10/2024) Started full-time at OpenAI, almost exactly three years after beginning my PhD journey at Oxford. Excited and grateful for the opportunity ahead!
(09/2024) Five papers at NeurIPS 2024 thanks to my amazing co-authors!
(08/2024) We released a blog post on The AI Scientist, our paper on fully automating AI research ideation, experimentation, and writing. The work was covered by multiple news outlets, including Nature, VentureBeat, Ars Technica, Wired, and Forbes.
(06/2024) Matthew T. Jackson and I were interviewed on the AutoML podcast!
(06/2024) We released a blog post on DiscoPOP, our paper on using LLM's to invent better algorithms to train LLM's.
(05/2024) We received a Gemma Academic Program grant, which I co-wrote on behalf of the lab.
(05/2024) I started an internship at Sakana AI as a Research Scientist!
(01/2024) I gave talks invited on JaxMARL and PureJaxRL at the Berkeley Multi-Agent Learning Seminar and the BeNeRL seminar series.
(11/2023) We released a blog post on JaxMARL, our JAX-based Multi-Agent RL library.
(04/2023) I released a blog post on PureJaxRL, a JAX-based RL library that massively speeds up RL training.
(03/2023) I gave talks on evolutionary meta-learning at the UMD Multi-Agent Reinforcement Learning Reading Group, the AI4ABM Reading Group, and the Learning in Foundation Environments (LIFE) Reading Group!
(09/2022) We received a GoodAI grant, which I co-wrote on behalf of the lab
(06/2022) I started an internship at DeepMind as a Research Scientist on the Discovery Team!
(05/2022) We received an Oracle for Research Grant, which I co-wrote on behalf of the lab
(10/2021) I started my DPhil at Oxford with FLAIR

Blog Posts

Highlighted Preprints

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
Chris Lu*, Cong Lu*, Robert Tjarko Lange*, Jakob Foerster^†, Jeff Clune^†, David Ha^†
*Equal Contribution, ^†Equal Advising
arXiv Preprint

arXiv | blog | code | tweet

Publications (representative papers are highlighted)

	Discovering Preference Optimization Algorithms with and for Large Language Models Chris Lu, Samuel Holt, Claudio Fanconi, Alex J. Chan, Jakob Foerster^†, Mihaela van der Schaar^†, Robert Tjarko Lange^† Equal Contribution, ^†Equal Advising NeurIPS 2024 AutoRL Workshop @ ICML 2024 arXiv \| blog \| code \| video?? \| tweet
	Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning Jonathan Cook, Chris Lu, Edward Hughes, Joel Z. Leibo, Jakob Foerster Equal Contribution NeurIPS 2024* arXiv \| code \| tweet
	JaxMARL: Multi-Agent RL Environments in JAX Alexander Rutherford^†, Benjamin Ellis^†, Matteo Gallici^†, Jonathan Cook^†, Andrei Lupu^†, Gardar Ingvarsson^†, Timon Willi^†, Ravi Hammond, Akbir Khan, Christian Schroeder de Witt, Alexandra Souly, Saptarashmi Bandyopadhyay, Mikayel Samvelyan, Minqi Jiang, Robert Tjarko Lange, Shimon Whiteson, Bruno Lacerda, Nick Hawes, Tim Rocktaschel, Chris Lu^†*, Jakob Nicolaus Foerster Equal Contribution, ^†Core Contribution NeurIPS 2024 Also at the NeurIPS 2023 Workshop on Agent Learning in Open-Endedness arXiv \| code \| tweet
	Revisiting Recurrent Reinforcement Learning with Memory Monoids Steven Morad, Chris Lu, Ryan Kortvelesy, Stephan Liwicki, Jakob Foerster, Amanda Prorok NeurIPS 2024 arXiv
	Can Learned Optimization Make Reinforcement Learning Less Difficult? Alexander David Goldie, Chris Lu, Matthew Thomas Jackson, Shimon Whiteson, Jakob Foerster NeurIPS 2024 (Spotlight) AutoRL Workshop @ ICML 2024 (Spotlight) arXiv \| code \| tweet
	JaxLife: An Open-Ended Agentic Simulator Chris Lu, Michael Beukman, Michael Matthews, Jakob Foerster Equal Contribution ALIFE 2024 (Oral)* paper \| code \| tweet (coming soon!)
	ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages Andrew Jesson, Chris Lu, Gunshi Gupta, Angelos Filos, Jakob Nicolaus Foerster, Yarin Gal ICML 2024 arXiv \| code \| tweet
	EvIL: Evolution Strategies for Generalisable Imitation Learning Silvia Sapora, Gokul Swamy, Chris Lu, Yee Whye Teh, Jakob Nicolaus Foerster ICML 2024 Also at the NeurIPS 2023 Robot Learning Workshop arXiv \| code \| tweet
	Discovering Temporally-Aware Reinforcement Learning Algorithms Matthew Jackson, Chris Lu, Louis Kirsch, Robert Lange, Shimon Whiteson, Jakob Foerster Equal Contribution ICLR 2024* Also at the NeurIPS 2023 Workshop on Agent Learning in Open-Endedness arXiv \| code \| tweet
	Behaviour Distillation Andrei Lupu, Chris Lu, Jarek Luca Liesen, Robert Tjarko Lange, Jakob Foerster ICLR 2024 paper \| code \| tweet
	Analyzing the Sample Complexity of Model-Free Opponent Shaping Kitty Fung, Qizhen Zhang, Chris Lu, Jia Wan, Timon Willi, Jakob Foerster Equal Contribution AAMAS 2024 (Oral)* Also at the ICML 2023 Workshop on New Frontiers in Learning, Control, and Dynamical Systems arXiv \| code
	Scaling Opponent Shaping to High Dimensional Games Akbir Khan, Timon Willi, Newton Kwan, Andrea Tachetti, Chris Lu, Edward Grefenstette, Tim Rocktäschel, Jakob Foerster Equal Contribution AAMAS 2024 (Oral) Also at the Games, Agents, and Incentives Workshop at AAMAS 2023 webpage \| arXiv \| code
	JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading Sascha Frey, Kang Li, Peer Nagy, Silvia Sapora, Chris Lu, Stefan Zohren, Jakob Foerster, Anisoara Calinescu Equal Contribution International Conference on AI in Finance 2023 (Best Academic Paper Award) arXiv \| code \| linkedin?
	Structured State Space Models for In-Context Reinforcement Learning Chris Lu, Yannick Schroecker, Albert Gu, Emilio Parisotto, Jakob Foerster, Satinder Singh, Feryal Behbahani NeurIPS 2023 Also at the Workshop on New Frontiers in Learning, Control, and Dynamical Systems @ ICML 2023 arXiv \| code \| tweet
	Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design Matthew Thomas Jackson, Minqi Jiang, Jack Parker-Holder, Risto Vuorio, Chris Lu, Gregory Farquhar, Shimon Whiteson, Jakob Nicolaus Foerster NeurIPS 2023 arXiv \| code \| tweet
	Adversarial Cheap Talk Chris Lu, Timon Willi, Alistair Letcher, Jakob Foerster ICML 2023 Also at the Workshop on Machine Learning for Cybersecurity @ ICML 2022 (Spotlight) webpage \| arXiv \| code \| tweet
	Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization Robert Tjarko Lange, Tom Schaul, Yutian Chen, Chris Lu, Tom Zahavy, Valentin Dallibard, Sebastian Flennerhag GECCO 2023 arXiv
	Arbitrary Order Meta-Learning with Simple Population-Based Evolution Chris Lu, Sebastian Towers, Jakob Foerster ALIFE 2023 (Oral) arXiv
	Discovering Evolution Strategies via Meta-Black-Box Optimization Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dallibard, Chris Lu, Satinder Singh, Sebastian Flennerhag ICLR 2023 arXiv \| video \| code \| tweet
	Discovered Policy Optimisation Chris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz, Christian Schroeder de Witt, Jakob Foerster Equal Contribution NeurIPS 2022* Also at the Decision Awareness in Reinforcement Learning Workshop @ ICML 2022 (Oral) arXiv \| video \| code \| tweet
	Proximal Learning With Opponent-Learning Awareness Stephen Zhao, Chris Lu, Roger Baker Grosse, Jakob Foerster NeurIPS 2022 arXiv \| code \| tweet
	Model-Free Opponent Shaping Chris Lu, Timon Willi, Christian Schroeder de Witt, Jakob Foerster ICML 2022 (Spotlight) Also at the ICLR 2022 Workshop on Gamification and Multiagent Solutions (Spotlight) arXiv \| video \| code \| tweet
	Centralized Model and Exploration Policy for Multi-Agent RL Qizhen Zhang, Chris Lu, Animesh Garg, Jakob Foerster AAMAS 2022 (Oral Presentation) arXiv \| video \| tweet
	Learning to Control Self-Assembling Morphologies Deepak Pathak, Chris Lu, Trevor Darrell, Phillip Isola, Alexei A. Efros Equal Contribution NeurIPS 2019 (Spotlight)* Winner of Virtual Creatures Competition (link) webpage \| arXiv \| video \| code \| tweet

Preprints and Workshop Papers

	Navix: Scaling MiniGrid Environments with JAX Eduardo Pignatelli, Jarek Liesen, Robert Tjarko Lange, Chris Lu, Pablo Samuel Castro, Laura Toni arXiv \| code \| tweet
	Meta-learning the Mirror Map in Policy Mirror Descent Carlo Alfano, Sebastian Towers, Silvia Sapora, Chris Lu, Patrick Rebeschini arXiv Preprint AutoRL Workshop @ ICML 2024 arXiv
	Leading the Pack: N-player Opponent Shaping Alexandra Souly, Timon Willi, Akbir Khan, Robert Kirk, Chris Lu, Edward Grefenstette, Tim Rocktäschel Multi-Agent Security Workshop @ NeurIPS 2023 (Oral) paper

Misc

Reviewer for: NeurIPS 2021, ICLR 2022, IROS 2022, NeurIPS 2022, NeurIPS 2023 (Top Reviewer), ICLR 2024, ICML 2024, NeurIPS 2024, ALOE@ICLR2022, DARL@ICML2022, AI4ABM@ICML2022, F4LCD@ICML2023, ALOE@NeurIPS 2023, AutoRL@ICML2024
In my free time I like to work on side projects. I used to sell kalimbas. I also solo-developed and sold a video game.
I also created the Noisy TV environment that appears in a few highly-cited papers on curiosity-driven learning. The code is here.
If you want to see some of my older works and projects, my old website is here.

Credit for the template to Jon Barron.