Title: ICML Invited Talk Proxy objectives in reinforcement learning from human feedback Description: No description Keywords: No keywords Text content: ICML Invited Talk Proxy objectives in reinforcement learning from human feedback ICML 2023 Skip to yearly menu bar Skip to main content Main Navigation ICML Help/FAQ Contact ICML Downloads Code of Conduct Create Profile Journal To Conference Track Diversity & Inclusion Privacy Policy Future Meetings Press Careers My Stuff Login Select Year: (2023) 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2002 1996 IMLS Archives Getting Started Schedule Tutorials Main Conference Invited Talks Orals Awards Test of Time Award Papers Workshops Community Affinity Events Socials Mentorship Town Hall / Business Meeting Sponsors Organizers Help FAQ Presenters Instructions Moderators Instructions RocketChat Help RocketChat Desktop Client Invited Talk Proxy objectives in reinforcement learning from human feedback John Schulman Moderator : Emma Brunskill Exhibit Hall 2 [ Abstract ] Abstract: Proxy objectives are a fundamental concept in machine learning. That is, there's a true objective that we care about, but it's hard to compute or estimate, so instead we construct a locally-valid approximation and optimize that. I will examine reinforcement from human feedback with this lens, as a chain of approximations, each of which can widen the gap between the desired and achieved result. Chat is not available. Successful Page Load ICML uses cookies for essential functions only. We do not sell your personal information. Our Privacy Policy »  Accept Cookies The ICML Logo above may be used on presentations. Right-click and choose download. It is a vector graphic and may be used at any scale. Useful links About ICML ICML Proceedings at PMLR Code of Conduct Contact 1269 Law Street, San Diego CA 92109 Email ICML Proceedings at PMLR