Thinking LLMs: General Instruction Following with Thought Generation
Authors: name: Tianhao Wu, name: Janice Lan, name: Weizhe Yuan, name: Jiantao Jiao, name: Jason Weston, name: Sainbayar Sukhbaatar

LLMs are typically trained to answer user questions or follow instructions
similarly to how human experts respond. However, in the standard alignment
framework they lack the basic ability of explicit thinking before answering.
Thinking is important for complex questions that require reasoning and planning
-- but can be applied to any task. We propose a training method for equipping
existing LLMs with such thinking abilities for general instruction following
without use of additional human data. We achieve this by an iterative search
and optimization procedure that explores the space of possible thought
generations, allowing the model to learn how to think without direct
supervision. For each instruction, the thought candidates are scored using a
judge model to evaluate their responses only, and then optimized via preference
optimization. We show that this procedure leads to superior performance on
AlpacaEval and Arena-Hard, and shows gains from thinking on non-reasoning
categories such as marketing, health and general knowledge, in addition to more
traditional reasoning & problem-solving tasks.