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border-[#828c97]"><div class="flex-col md:flex-row flex-1 justify-center items-center gap-0"><div class="flex flex-wrap"><section class="order-2 justify-center items-center lg:order-1 w-full lg:w-1/2 h-auto py-6 md:py-12 lg:py-12 bg-[#fffcf5] flex flex-1 lg:border-r border-[#828c97]"><div class="container"><div class="grid items-center"><div class="flex flex-col justify-center space-y-4 text-center"><div class="space-y-4"><ul class="text-base text-left list-inside p-4 rounded-lg space-y-4"><li>Post-training is unlocking new capabilities in foundation models.</li><li>Training on raw human data doesn’t scale.</li><li>Current methods of “alignment” are insufficient; <br/>evaluations are even worse.</li><li>Human intent is rich in preferences, collapsed by uniform models.</li><li>AI`s potential hinges on trust, from interpretable data to every layer built upon it.</li><li>Your models should adapt and scale, automatically.</li></ul></div></div></div></div></section><section class="w-full lg:w-1/2 flex justify-center items-center py-6 md:py-12 lg:py-24 xl:py-32 order-1 border-b lg:border-b-0 border-[#828c97]"><div class="text-center"><div class="flex justify-center items-center"><div class="relative p-6 bg-[#ffc0cb] border-4 border-black w-full max-w-md mx-auto"><h2 class="text-2xl md:text-3xl font-bold my-8 md:mb-12">Frontier challenges in AI post-training</h2></div></div></div></section></div></div></div></div></div></div></div><div class="bg-[#fffcf5] text-black" style="margin:-1px"><div class="border-y border-[#828c97]"><div class="mx-auto max-w-7xl"><div class="w-full flex border-x border-[#828c97]"><div class="border-r border-[#828c97] bg-[#fffcf5] p-4 align-middle text-center justify-center items-center w-3/4 sm:w-3/4 md:w-3/4 lg:w-3/4" style="min-height:150px"><div><div class="border text-card-foreground max-w-xl mx-auto bg-white shadow-lg rounded-lg overflow-hidden"><div class="bg-[#8b0000] p-4"><div class="text-white text-sm">Computer Science > Machine Learning</div></div><div class="p-6 pt-0"><div class="px-6 py-4"><div class="font-bold text-xl mb-2">Generative Reward Models</div><p class="text-gray-700 text-base">Dakota Mahan, Duy Van Phung, Rafael Rafailov, Chase Blagden, Nathan Lile, Louis Castricato, Jan-Philipp Fränken, Chelsea Finn, Alon Albalak</p><p class="text-gray-700 text-base mt-4">Reinforcement Learning from Human Feedback (RLHF) has greatly improved the performance of modern Large Language Models (LLMs). The RLHF process is resource-intensive and technically challenging, generally requiring a large collection of human preference labels over model-generated outputs. Reinforcement Learning from AI Feedback (RLAIF) addresses this data collection challenge by leveraging synthetic preferences generated by an LLM. However, recent work has shown that synthetic preferences labels may not align well with human preference judgments. To address this, we propose a hybrid approach that unifies RLHF and RLAIF methodologies.</p></div></div><div class="p-6 px-6 py-4 bg-gray-100 text-sm flex justify-between items-center"><div>Submitted on 2 Oct 2024</div><button class="isolate py-2 inline-flex gap-2 items-center justify-center whitespace-nowrap text-sm font-medium ring-offset-background transition-colors focus-visible:outline-none disabled:opacity-50 disabled:transition-none disabled:hover:text-unset rounded-md group border-black border-2 after:content-[''] after:absolute after:translate-y-full hover:after:translate-y-0 after:transform-all after:w-full after:h-full after:ease-out after:duration-300 after:z-[-10] overflow-hidden translate-y-[-2px] hover:translate-y-0 shadow-black-xl-br hover:shadow-black-md-br h-11 px-8 bg-[#8b0000] text-white"><a target="_blank" rel="noopener noreferrer" href="https://arxiv.org/abs/2410.12832">Read PDF</a></button></div></div></div></div><div class="bg-[#fffcf5] p-4 align-middle text-center justify-center items-center w-1/4 sm:w-1/4 md:w-1/4 lg:w-1/4" style="min-height:150px"><div class="flex flex-col items-center justify-center h-full space-y-4"><div class="text-sm font-mono uppercase">Use Cases</div><a class="inline-block bg-white px-8 py-4 text-lg font-bold border-4 border-black shadow-[4px_4px_0_0_rgba(0,0,0,0.7)] hover:shadow-[6px_6px_0_0_rgba(0,0,0,0.7)] hover:translate-x-[-2px] hover:translate-y-[-2px] transition-all" href="/research/generative-reward-models"><span class="font-mono">Blog Post</span></a></div></div></div></div></div></div><div class="bg-[#FCFD9C] py-8 px-4 md:px-8"><div class="mx-auto max-w-7xl grid grid-cols-1 md:grid-cols-3 gap-8 md:gap-12"><div class="md:col-span-1"><div class="text-3xl sm:text-3xl md:text-4xl lg:text-5xl font-bold leading-tight text-gray-900">EleutherAI said it best</div></div><div class="md:col-span-2 mt-4 md:mt-0"><p class="text-xl text-gray-700">Democratizing AI research is essential, as the future of transformative technologies should not be confined to the corridors of a few profit-driven entities, but open to independent inquiry and understanding for the collective good.</p><div class="overflow-hidden cursor-pointer"><div class="flex justify-left my-4"><div class="flex justify-left"><span data-tally-open="m62N4Y" data-tally-hide-title="1" data-tally-emoji-text="🔎" data-tally-emoji-animation="wave"><span class="inset-0" aria-hidden="true" data-tally-open="3ldr06" data-tally-hide-title="1"></span><div class="text-xl font-medium underline decoration-pink-500">Let's collaborate on open science ML research →</div><span aria-hidden="true"></span></span></div></div></div></div></div></div><div class="bg-[#fffcf5] text-black" style="margin:-1px"><div class="border-y border-[#828c97]"><div class="mx-auto max-w-7xl"><div class="w-full flex border-x border-[#828c97]"><div class="border-r border-[#828c97] bg-[#fffcf5] p-4 align-middle text-center justify-center items-center w-1/2 sm:w-1/2 md:w-1/2 lg:w-1/2" style="min-height:150px"><p></p></div><div class="bg-[#fffcf5] p-4 align-middle text-center justify-center items-center w-1/2 sm:w-1/2 md:w-1/2 lg:w-1/2" style="min-height:150px"><div class="max-w-xl mx-auto"><iframe data-tally-src="https://tally.so/embed/3ldr06?alignLeft=1&transparentBackground=1&dynamicHeight=1" width="100%" height="284" title="Join our Community"></iframe></div></div></div></div></div></div><div class="bg-[#fffcf5] text-black" style="margin:-1px"><div class="border-y border-[#828c97]"><div class="mx-auto max-w-7xl"><div class="w-full flex border-x border-[#828c97]"><div class="border-r border-[#828c97] bg-[#fffcf5] p-4 align-middle text-center justify-center items-center w-9/10 sm:w-9/10 md:w-9/10 lg:w-9/10" style="min-height:150px"><span></span></div><div class="bg-[#fffcf5] p-4 align-middle text-center justify-center items-center w-1/10 sm:w-1/10 md:w-1/10 lg:w-1/10" style="min-height:150px"><div><div class="border text-card-foreground max-w-xl mx-auto bg-white shadow-lg rounded-lg overflow-hidden"><div class="bg-[#8b0000] p-4"><div class="text-white text-sm">Computer Science > Machine Learning</div></div><div class="p-6 pt-0"><div class="px-6 py-4"><div class="font-bold text-xl mb-2">Supressing Pink Elephants with Direct Principle Feedback</div><p class="text-gray-700 text-base">Louis Castricato, Nathan Lile, Suraj Anand, Hailey Schoelkopf, Siddharth Verma, and Stella Biderman</p><p class="text-gray-700 text-base mt-4">Existing methods for controlling language models, such as RLHF and Constitutional AI, involve determining which LLM behaviors are desirable and training them into a language model. However, in many cases, it is desirable for LLMs to be controllable at inference time, so that they can be used in multiple contexts with diverse needs. We illustrate this with the Pink Elephant Problem: instructing an LLM to avoid discussing a certain entity (a “Pink Elephant”), and instead discuss a preferred entity (“Grey Elephant”).</p></div></div><div class="p-6 px-6 py-4 bg-gray-100 text-sm flex justify-between items-center"><div>Submitted on 12 Feb 2024</div><button class="isolate py-2 inline-flex gap-2 items-center justify-center whitespace-nowrap text-sm font-medium ring-offset-background transition-colors focus-visible:outline-none disabled:opacity-50 disabled:transition-none disabled:hover:text-unset rounded-md group border-black border-2 after:content-[''] after:absolute after:translate-y-full hover:after:translate-y-0 after:transform-all after:w-full after:h-full after:ease-out after:duration-300 after:z-[-10] overflow-hidden translate-y-[-2px] hover:translate-y-0 shadow-black-xl-br hover:shadow-black-md-br h-11 px-8 bg-[#8b0000] text-white"><a target="_blank" rel="noopener noreferrer" href="https://arxiv.org/abs/2402.07896">Read PDF</a></button></div></div></div></div></div></div></div></div><div class="relative border-y border-gray-600 p-1 -m-1 text-black bg-blue-200 mt-12"><div class="flex justify-center py-2"><span class="text-sm font-mono uppercase"><h3 class="__className_7dd02c text-xl m-4 md:m-2 lg:m-0">Supported By</h3></span></div></div><div class="relative border-y border-gray-600 bg-[#fff] text-black p-1 -m-1"><div class="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-6 gap-4 px-4 pt-4 md:mt-4"><div class="col-span-1 hidden lg:block"></div><div class="flex flex-col lg:col-span-2 items-center"><img alt="Microsoft’s M12 Ventures" loading="lazy" width="250" height="250" decoding="async" data-nimg="1" style="color:transparent" srcSet="/_next/image?url=%2Fimg%2Fsupported-by%2Fm12.svg.png&w=256&q=75 1x, /_next/image?url=%2Fimg%2Fsupported-by%2Fm12.svg.png&w=640&q=75 2x" src="/_next/image?url=%2Fimg%2Fsupported-by%2Fm12.svg.png&w=640&q=75"/></div><div class="flex flex-col lg:col-span-2 items-center"><img alt="Eric Schmidt's First Spark Ventures" loading="lazy" width="250" height="250" decoding="async" data-nimg="1" style="color:transparent" srcSet="/_next/image?url=%2Fimg%2Fsupported-by%2Ffsv.svg.png&w=256&q=75 1x, /_next/image?url=%2Fimg%2Fsupported-by%2Ffsv.svg.png&w=640&q=75 2x" src="/_next/image?url=%2Fimg%2Fsupported-by%2Ffsv.svg.png&w=640&q=75"/></div><div class="col-span-1 hidden lg:block"></div></div><div class="grid grid-cols-1 md:grid-cols-6 lg:grid-cols-12 gap-4 md:gap-10 pb-4 px-4 lg:px-8 mb-8 text-center"><div class="lg:col-start-4 lg:col-span-3 md:col-start-2 md:col-span-4 col-span-1 p-1 pb-2 text-gray-500 rounded"><h4 class="mt-2">[ mei ventures ]</h4></div><div class="lg:col-start-7 lg:col-span-3 md:col-start-2 md:col-span-4 col-span-1 p-1 pb-2 text-gray-500 rounded"><h4 class="mt-2">[ ashish vaswani ]</h4></div></div></div></div></div><footer class="bg-[#272635] text-white p-6"><div class="flex flex-col sm:flex-row 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