Measuring Compositional Generalization: A Comprehensive Method on Realistic Data. - Daniel Keysers; Nathanael Schärli; Nathan Scales; Hylke Buisman; Daniel Furrer; Sergii Kashubin; Nikola Momchev; Danila Sinopalnikov; Lukasz Stafiniak; Tibor Tihon; Dmitry Tsarkov; Xiao Wang 038; et al. (ICLR, 2020)

*-CFQ: Analyzing the Scalability of Machine Learning on a Compositional Task. - Dmitry Tsarkov; Tibor Tihon; Nathan Scales; Nikola Momchev; Danila Sinopalnikov; Nathanael Schärli (AAAI, 2021)

BOND: Aligning LLMs with Best-of-N Distillation. - Pier Giuseppe Sessa; Robert Dadashi; Léonard Hussenot; Johan Ferret; Nino Vieillard; Alexandre Ramé; Bobak Shahriari; Sarah Perrin; Abe Friesen; Geoffrey Cideron; Sertan Girgin; Piotr Stanczyk; et al. (CoRR, 2024)

Gemma 2: Improving Open Language Models at a Practical Size. - Morgane Rivière; Shreya Pathak; Pier Giuseppe Sessa; Cassidy Hardin; Surya Bhupatiraju; Léonard Hussenot; Thomas Mesnard; Bobak Shahriari; Alexandre Ramé; Johan Ferret; Peter Liu; Pouya Tafti; et al. (CoRR, 2024)

BOND: Aligning LLMs with Best-of-N Distillation. - Pier Giuseppe Sessa; Robert Dadashi-Tazehozi; Léonard Hussenot; Johan Ferret; Nino Vieillard; Alexandre Ramé; Bobak Shahriari; Sarah Perrin; Abram L. Friesen; Geoffrey Cideron; Sertan Girgin; Piotr Stanczyk; et al. (ICLR, 2025)

Gemma 3 Technical Report. - Aishwarya Kamath; Johan Ferret; Shreya Pathak; Nino Vieillard; Ramona Merhej; Sarah Perrin; Tatiana Matejovicova; Alexandre Ramé; Morgane Rivière; Louis Rouillard; Thomas Mesnard; Geoffrey Cideron; et al. (CoRR, 2025)