Measuring the Reliability of Reinforcement Learning Algorithms. - Stephanie C. Y. Chan; Samuel Fishman; Anoop Korattikara; John F. Canny; Sergio Guadarrama (ICLR, 2020)

Towards mental time travel: a hierarchical memory for reinforcement learning agents. - Andrew K. Lampinen; Stephanie C. Y. Chan; Andrea Banino; Felix Hill (NeurIPS, 2021)

Zipfian Environments for Reinforcement Learning. - Stephanie C. Y. Chan; Andrew Kyle Lampinen; Pierre Harvey Richemond; Felix Hill (CoLLAs, 2022)

Can language models learn from explanations in context? - Andrew K. Lampinen; Ishita Dasgupta 001; Stephanie C. Y. Chan; Kory W. Mathewson; Michael Henry Tessler; Antonia Creswell; James L. McClelland; Jane Wang 001; Felix Hill (EMNLP (Findings), 2022)

Tell me why! Explanations support learning relational and causal structure. - Andrew K. Lampinen; Nicholas A. Roy; Ishita Dasgupta 001; Stephanie C. Y. Chan; Allison C. Tam; James L. McClelland; Chen Yan; Adam Santoro; Neil C. Rabinowitz; Jane X. Wang; Felix Hill (ICML, 2022)

Data Distributional Properties Drive Emergent In-Context Learning in Transformers. - Stephanie C. Y. Chan; Adam Santoro; Andrew K. Lampinen; Jane X. Wang; Aaditya K. Singh; Pierre H. Richemond; James L. McClelland; Felix Hill (NeurIPS, 2022)

Semantic Exploration from Language Abstractions and Pretrained Representations. - Allison C. Tam; Neil C. Rabinowitz; Andrew K. Lampinen; Nicholas A. Roy; Stephanie C. Y. Chan; DJ Strouse; Jane Wang 001; Andrea Banino; Felix Hill (NeurIPS, 2022)

Language models show human-like content effects on reasoning. - Ishita Dasgupta 001; Andrew K. Lampinen; Stephanie C. Y. Chan; Antonia Creswell; Dharshan Kumaran; James L. McClelland; Felix Hill (CoRR, 2022)

Transformers generalize differently from information stored in context vs in weights. - Stephanie C. Y. Chan; Ishita Dasgupta 001; Junkyung Kim; Dharshan Kumaran; Andrew K. Lampinen; Felix Hill (CoRR, 2022)

Passive learning of active causal strategies in agents and language models. - Andrew K. Lampinen; Stephanie C. Y. Chan; Ishita Dasgupta 001; Andrew J. Nam; Jane X. Wang (NeurIPS, 2023)

The Transient Nature of Emergent In-Context Learning in Transformers. - Aaditya K. Singh; Stephanie C. Y. Chan; Ted Moskovitz; Erin Grant; Andrew M. Saxe; Felix Hill (NeurIPS, 2023)

Genie: Generative Interactive Environments. - Jake Bruce; Michael D. Dennis; Ashley Edwards; Jack Parker-Holder; Yuge Shi; Edward Hughes 001; Matthew Lai; Aditi Mavalankar; Richie Steigerwald; Chris Apps; Yusuf Aytar; Sarah Bechtle; et al. (ICML, 2024)

What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation. - Aaditya K. Singh; Ted Moskovitz; Felix Hill; Stephanie C. Y. Chan; Andrew M. Saxe (ICML, 2024)

Many-Shot In-Context Learning. - Rishabh Agarwal; Avi Singh; Lei Zhang; Bernd Bohnet; Luis Rosias; Stephanie C. Y. Chan; Biao Zhang; Ankesh Anand; Zaheer Abbas; Azade Nova; John D. Co-Reyes; Eric Chu; et al. (NeurIPS, 2024)

Scaling Instructable Agents Across Many Simulated Worlds. - SIMA Team; Maria Abi Raad; Arun Ahuja; Catarina Barros; Frederic Besse; Andrew Bolt; Adrian Bolton; Bethanie Brownfield; Gavin Buttimore; Max Cant; Sarah Chakera; Stephanie C. Y. Chan; et al. (CoRR, 2024)

Many-Shot In-Context Learning. - Rishabh Agarwal; Avi Singh; Lei M. Zhang; Bernd Bohnet; Stephanie Chan 003; Ankesh Anand; Zaheer Abbas; Azade Nova; John D. Co-Reyes; Eric Chu; Feryal M. P. Behbahani; Aleksandra Faust; et al. (CoRR, 2024)

Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach. - Irina Jurenka; Markus Kunesch; Kevin R. McKee; Daniel Gillick; Shaojian Zhu; Sara Wiltberger; Shubham Milind Phal; Katherine L. Hermann; Daniel Kasenberg; Avishkar Bhoopchand; Ankit Anand; Miruna Pîslar; et al. (CoRR, 2024)

The broader spectrum of in-context learning. - Andrew Kyle Lampinen; Stephanie C. Y. Chan; Aaditya K. Singh; Murray Shanahan (CoRR, 2024)

LearnLM: Improving Gemini for Learning. - Abhinit Modi; Aditya Srikanth Veerubhotla; Aliya Rysbek; Andrea Huber; Brett Wiltshire; Brian Veprek; Daniel Gillick; Daniel Kasenberg; Derek Ahmed; Irina Jurenka; James Cohan; Jennifer She; et al. (CoRR, 2024)

Learned feature representations are biased by complexity, learning order, position, and more. - Andrew Kyle Lampinen; Stephanie C. Y. Chan; Katherine L. Hermann (Trans. Mach. Learn. Res., 2024)

Strategy Coopetition Explains the Emergence and Transience of In-Context Learning. - Aaditya K. Singh; Ted Moskovitz; Sara Dragutinovic; Felix Hill; Stephanie C. Y. Chan; Andrew M. Saxe (CoRR, 2025)

How do language models learn facts? Dynamics, curricula and hallucinations. - Nicolas Zucchet; Jörg Bornschein; Stephanie C. Y. Chan; Andrew K. Lampinen; Razvan Pascanu; Soham De (CoRR, 2025)

On the generalization of language models from in-context learning and finetuning: a controlled study. - Andrew K. Lampinen; Arslan Chaudhry; Stephanie C. Y. Chan; Cody Wild; Diane Wan; Alex Ku; Jörg Bornschein; Razvan Pascanu; Murray Shanahan; James L. McClelland (CoRR, 2025)

Predictability Shapes Adaptation: An Evolutionary Perspective on Modes of Learning in Transformers. - Alexander Y. Ku; Thomas L. Griffiths 001; Stephanie C. Y. Chan (CoRR, 2025)

The emergence of sparse attention: impact of data distribution and benefits of repetition. - Nicolas Zucchet; Francesco D'Angelo; Andrew K. Lampinen; Stephanie C. Y. Chan (CoRR, 2025)

Representation biases: will we achieve complete understanding by analyzing representations? - Andrew Kyle Lampinen; Stephanie C. Y. Chan; Yuxuan Li; Katherine L. Hermann (CoRR, 2025)