title: LearnLM: Improving Gemini for Learning
summary: Today's generative AI systems are tuned to present information by default
  rather than engage users in service of learning as a human tutor would. To
  address the wide range of potential education use cases for these systems, we
  reframe the challenge of injecting pedagogical behavior as one of
  pedagogical instruction following, where training and evaluation
  examples include system-level instructions describing the specific pedagogy
  attributes present or desired in subsequent model turns. This framing avoids
  committing our models to any particular definition of pedagogy, and instead
  allows teachers or developers to specify desired model behavior. It also clears
  a path to improving Gemini models for learning -- by enabling the addition of
  our pedagogical data to post-training mixtures -- alongside their rapidly
  expanding set of capabilities. Both represent important changes from our
  initial tech report. We show how training with pedagogical instruction
  following produces a LearnLM model (available on Google AI Studio) that is
  preferred substantially by expert raters across a diverse set of learning
  scenarios, with average preference strengths of 31\% over GPT-4o, 11\% over
  Claude 3.5, and 13\% over the Gemini 1.5 Pro model LearnLM was based on.
paper_authors._id: 676a61bda89cd26e3da31478
paper_authors.name: LearnLM Team
paper_authors._id: 676a61bda89cd26e3da31479
paper_authors.name: Abhinit Modi
paper_authors._id: 676a61bda89cd26e3da3147a
paper_authors.name: Aditya Srikanth Veerubhotla
paper_authors._id: 676a61bda89cd26e3da3147b
paper_authors.name: Aliya Rysbek
paper_authors._id: 676a61bda89cd26e3da3147c
paper_authors.name: Andrea Huber
paper_authors._id: 676a61bda89cd26e3da3147d
paper_authors.name: Brett Wiltshire
paper_authors._id: 676a61bda89cd26e3da3147e
paper_authors.name: Brian Veprek
paper_authors._id: 676a61bda89cd26e3da3147f
paper_authors.name: Daniel Gillick
paper_authors._id: 676a61bda89cd26e3da31480
paper_authors.name: Daniel Kasenberg
paper_authors._id: 676a61bda89cd26e3da31481
paper_authors.name: Derek Ahmed
paper_authors._id: 676a61bda89cd26e3da31482
paper_authors.name: Irina Jurenka
paper_authors._id: 676a61bda89cd26e3da31483
paper_authors.name: James Cohan
paper_authors._id: 676a61bda89cd26e3da31484
paper_authors.name: Jennifer She
paper_authors._id: 676a61bda89cd26e3da31485
paper_authors.name: Julia Wilkowski
paper_authors._id: 676a61bda89cd26e3da31486
paper_authors.name: Kaiz Alarakyia
paper_authors._id: 676a61bda89cd26e3da31487
paper_authors.name: Kevin McKee
paper_authors._id: 676a61bda89cd26e3da31488
paper_authors.name: Lisa Wang
paper_authors._id: 676a61bda89cd26e3da31489
paper_authors.name: Markus Kunesch
paper_authors._id: 676a61bda89cd26e3da3148a
paper_authors.name: Mike Schaekermann
paper_authors._id: 676a61bda89cd26e3da3148b
paper_authors.name: Miruna Pîslar
paper_authors._id: 676a61bda89cd26e3da3148c
paper_authors.name: Nikhil Joshi
paper_authors._id: 676a61bda89cd26e3da3148d
paper_authors.name: Parsa Mahmoudieh
paper_authors._id: 676a61bda89cd26e3da3148e
paper_authors.name: Paul Jhun
paper_authors._id: 676a61bda89cd26e3da3148f
paper_authors.name: Sara Wiltberger
paper_authors._id: 676a61bda89cd26e3da31490
paper_authors.name: Shakir Mohamed
paper_authors._id: 676a61bda89cd26e3da31491
paper_authors.name: Shashank Agarwal
paper_authors._id: 676a61bda89cd26e3da31492
paper_authors.name: Shubham Milind Phal
paper_authors._id: 676a61bda89cd26e3da31493
paper_authors.name: Sun Jae Lee
paper_authors._id: 676a61bda89cd26e3da31494
paper_authors.name: Theofilos Strinopoulos
paper_authors._id: 676a61bda89cd26e3da31495
paper_authors.name: Wei-Jen Ko
paper_authors._id: 676a61bda89cd26e3da31496
paper_authors.name: Amy Wang
paper_authors._id: 676a61bda89cd26e3da31497
paper_authors.name: Ankit Anand
paper_authors._id: 676a61bda89cd26e3da31498
paper_authors.name: Avishkar Bhoopchand
paper_authors._id: 676a61bda89cd26e3da31499
paper_authors.name: Dan Wild
paper_authors._id: 676a61bda89cd26e3da3149a
paper_authors.name: Divya Pandya
paper_authors._id: 676a61bda89cd26e3da3149b
paper_authors.name: Filip Bar
paper_authors._id: 676a61bda89cd26e3da3149c
paper_authors.name: Garth Graham
paper_authors._id: 676a61bda89cd26e3da3149d
paper_authors.name: Holger Winnemoeller
paper_authors._id: 676a61bda89cd26e3da3149e
paper_authors.name: Mahvish Nagda
paper_authors._id: 676a61bda89cd26e3da3149f
paper_authors.name: Prateek Kolhar
paper_authors._id: 676a61bda89cd26e3da314a0
paper_authors.name: Renee Schneider
paper_authors._id: 676a61bda89cd26e3da314a1
paper_authors.name: Shaojian Zhu
paper_authors._id: 676a61bda89cd26e3da314a2
paper_authors.name: Stephanie Chan
paper_authors._id: 676a61bda89cd26e3da314a3
paper_authors.name: Steve Yadlowsky
paper_authors._id: 676a61bda89cd26e3da314a4
paper_authors.name: Viknesh Sounderajah
paper_authors._id: 676a61bda89cd26e3da314a5
paper_authors.name: Yannis Assael
paper_title: LearnLM: Improving Gemini for Learning
paper_summary: Today's generative AI systems are tuned to present information by default
  rather than engage users in service of learning as a human tutor would. To
  address the wide range of potential education use cases for these systems, we
  reframe the challenge of injecting pedagogical behavior as one of
  pedagogical instruction following, where training and evaluation
  examples include system-level instructions describing the specific pedagogy
  attributes present or desired in subsequent model turns. This framing avoids
  committing our models to any particular definition of pedagogy, and instead
  allows teachers or developers to specify desired model behavior. It also clears
  a path to improving Gemini models for learning -- by enabling the addition of
  our pedagogical data to post-training mixtures -- alongside their rapidly
  expanding set of capabilities. Both represent important changes from our
  initial tech report. We show how training with pedagogical instruction
  following produces a LearnLM model (available on Google AI Studio) that is
  preferred substantially by expert raters across a diverse set of learning
  scenarios, with average preference strengths of 31\% over GPT-4o, 11\% over
  Claude 3.5, and 13\% over the Gemini 1.5 Pro model LearnLM was based on.