56 citations · 266 across the 35 of their papers we have counts for
3 papers · 2 filters
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…