Reasoning Language Models: A Blueprint
Maciej Besta, Julia Barth, Eric Schreiber, Ales Kubicek, Afonso Catarino, Robert Gerstenberger, Piotr Nyczyk, Patrick Iff, Yueling Li, Sam Houliston, Tomasz Sternal, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Łukasz Flis, Hannes Eberhard, Hubert Niewiadomski, Torsten Hoefler
2025-01-22
Summary
This paper talks about a new way to build and understand AI models that can reason, called Reasoning Language Models (RLMs). The researchers created a blueprint to help make these complex AI systems easier to build and use.
What's the problem?
RLMs are really good at solving problems, but they're expensive to make and hard for most people to use or understand. Only big tech companies can usually afford to create them, which means not everyone can benefit from this technology. The way RLMs are built is also really complicated, mixing different AI techniques in ways that are hard to figure out.
What's the solution?
The researchers came up with a blueprint that breaks down how RLMs work into smaller, easier-to-understand parts. They looked at all the different ways people have made RLMs and organized these ideas into a framework anyone can use. This blueprint includes different ways of structuring how the AI thinks (like in chains or trees), strategies for making decisions, and methods for training the AI. They also created a tool called x1 that lets people experiment with building their own RLMs more easily. The researchers tested their ideas and found some helpful tips, like training the AI in multiple stages and using familiar types of data to teach it.
Why it matters?
This research matters because it could help more people create and use powerful AI that can reason. By making RLMs easier to understand and build, the researchers are trying to close the gap between big tech companies and everyone else in AI development. This could lead to more innovation and allow more people to benefit from AI that can solve complex problems. It's like giving everyone a recipe and tools to bake an advanced cake, instead of only professional bakers being able to make it.
Abstract
Reasoning language models (RLMs), also known as Large Reasoning Models (LRMs), such as OpenAI's o1 and o3, DeepSeek-V3, and Alibaba's QwQ, have redefined AI's problem-solving capabilities by extending large language models (LLMs) with advanced reasoning mechanisms. Yet, their high costs, proprietary nature, and complex architectures - uniquely combining Reinforcement Learning (RL), search heuristics, and LLMs - present accessibility and scalability challenges. To address these, we propose a comprehensive blueprint that organizes RLM components into a modular framework, based on a survey and analysis of all RLM works. This blueprint incorporates diverse reasoning structures (chains, trees, graphs, and nested forms), reasoning strategies (e.g., Monte Carlo Tree Search, Beam Search), RL concepts (policy, value models and others), and supervision schemes (Output-Based and Process-Based Supervision). We also provide detailed mathematical formulations and algorithmic specifications to simplify RLM implementation. By showing how schemes like LLaMA-Berry, QwQ, Journey Learning, and Graph of Thoughts fit as special cases, we demonstrate the blueprint's versatility and unifying potential. To illustrate its utility, we introduce x1, a modular implementation for rapid RLM prototyping and experimentation. Using x1 and a literature review, we provide key insights, such as multi-phase training for policy and value models, and the importance of familiar training distributions. Finally, we outline how RLMs can integrate with a broader LLM ecosystem, including tools and databases. Our work demystifies RLM construction, democratizes advanced reasoning capabilities, and fosters innovation, aiming to mitigate the gap between "rich AI" and "poor AI" by lowering barriers to RLM development and experimentation.