56 citations · 266 across the 35 of their papers we have counts for
7 papers · 1 filter
The History and Risks of Reinforcement Learning and Human Feedback
Nathan Lambert, Thomas Krendl Gilbert, Tom Zick
Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique to make large language models (LLMs) easier to use and more effective. A core piece of the RLH…
Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2
Hamish Ivison, Yizhong Wang, Valentina Pyatkin +8
Since the release of TÜLU [Wang et al., 2023b], open resources for instruction tuning have developed quickly, from better base models to new finetuning techniques. We test and inco…
The Alignment Ceiling: Objective Mismatch in Reinforcement Learning from Human Feedback
Nathan Lambert, Roberto Calandra
Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique to make large language models (LLMs) more capable in complex settings. RLHF proceeds as collec…
Zephyr: Direct Distillation of LM Alignment
Lewis Tunstall, Edward Beeching, Nathan Lambert +11
We aim to produce a smaller language model that is aligned to user intent. Previous research has shown that applying distilled supervised fine-tuning (dSFT) on larger models signif…
A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning
Ran Wei, Nathan Lambert, Anthony McDonald +2
Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient, adaptive, and explainable by learning an explicit model of the environment. While the capabilit…
Confidence-Building Measures for Artificial Intelligence: Workshop Proceedings
Sarah Shoker, Andrew Reddie, Sarah Barrington +20
Foundation models could eventually introduce several pathways for undermining state security: accidents, inadvertent escalation, unintentional conflict, the proliferation of weapon…