Reasoning Language Models: A Blueprint

• URL: http://arxiv.org/abs/2501.11223v3
• Date: Thu, 23 Jan 2025 14:26:08 GMT
• Title: Reasoning Language Models: A Blueprint
• Authors: 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,
• Abstract summary: Reasoning language models (RLMs) have redefined AI's problem-solving capabilities.<n>Yet, their high costs, proprietary nature, and complex architectures present accessibility and scalability challenges.<n>We propose a comprehensive blueprint that organizes RLM into a modular framework.
• Score: 12.966875494760785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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), supervision schemes (Outcome-Based and Process-Based Supervision), and other related concepts (e.g., Test-Time Compute, Retrieval-Augmented Generation, agent tools). 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 discuss scalable RLM cloud deployments and we outline how RLMs can integrate with a broader LLM ecosystem. 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 design and experimentation.

Related papers

• Modular Machine Learning: An Indispensable Path towards New-Generation Large Language Models [45.05285463251872]
  We introduce a novel learning paradigm -- Modular Machine Learning (MML) -- as an essential approach toward new-generation large language models (LLMs) MML decomposes the complex structure of LLMs into three interdependent components: modular representation, modular model, and modular reasoning. We present a feasible implementation of MML-based LLMs via leveraging advanced techniques such as disentangled representation learning, neural architecture search and neuro-symbolic learning.
  arXiv  Detail & Related papers  (2025-04-28T17:42:02Z)
• Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey [2.572335031488049]
  This survey explores the development of meta-thinking capabilities in Large Language Models (LLMs) from a Multi-Agent Reinforcement Learning (MARL) perspective. By exploring reward mechanisms, self-play, and continuous learning methods in MARL, this survey gives a comprehensive roadmap to building introspective, adaptive, and trustworthy LLMs.
  arXiv  Detail & Related papers  (2025-04-20T07:34:26Z)
• R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning [87.30285670315334]
  textbfR1-Searcher is a novel two-stage outcome-based RL approach designed to enhance the search capabilities of Large Language Models. Our framework relies exclusively on RL, without requiring process rewards or distillation for a cold start. Our experiments demonstrate that our method significantly outperforms previous strong RAG methods, even when compared to the closed-source GPT-4o-mini.
  arXiv  Detail & Related papers  (2025-03-07T17:14:44Z)
• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks. However, they still struggle with problems requiring multi-step decision-making and environmental feedback. We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search [57.28671084993782]
  Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains. Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities. We propose a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning.
  arXiv  Detail & Related papers  (2025-02-04T17:26:58Z)
• APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World Agents [8.479128275067742]
  We present an advanced Large Language Model (LLM)-driven framework that enables autonomous agents to construct complex structures in Minecraft.<n>By employing chain-of-thought decomposition along with multimodal inputs, the framework generates detailed architectural layouts and blueprints.<n>Our agent incorporates both memory and reflection modules to facilitate lifelong learning, adaptive refinement, and error correction throughout the building process.
  arXiv  Detail & Related papers  (2024-11-26T09:31:28Z)
• Unconstrained Model Merging for Enhanced LLM Reasoning [42.079040543428036]
  We explore the potential of merging multiple expert models into a single large language model. We propose an unconstrained model merging framework that accommodates both homogeneous and heterogeneous model architectures. Across 7 benchmarks and 9 reasoning-optimized LLMs, we reveal key findings that reasoning emerges from merging.
  arXiv  Detail & Related papers  (2024-10-17T16:04:07Z)
• Configurable Foundation Models: Building LLMs from a Modular Perspective [115.63847606634268]
  A growing tendency to decompose LLMs into numerous functional modules allows for inference with part of modules and dynamic assembly of modules to tackle complex tasks. We coin the term brick to represent each functional module, designating the modularized structure as customizable foundation models. We present four brick-oriented operations: retrieval and routing, merging, updating, and growing. We find that the FFN layers follow modular patterns with functional specialization of neurons and functional neuron partitions.
  arXiv  Detail & Related papers  (2024-09-04T17:01:02Z)
• Multi-step Inference over Unstructured Data [2.169874047093392]
  High-stakes decision-making tasks in fields such as medical, legal and finance require a level of precision, comprehensiveness, and logical consistency. We have developed a neuro-symbolic AI platform to tackle these problems. The platform integrates fine-tuned LLMs for knowledge extraction and alignment with a robust symbolic reasoning engine.
  arXiv  Detail & Related papers  (2024-06-26T00:00:45Z)
• A Survey on Self-Evolution of Large Language Models [116.54238664264928]
  Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications. To address this issue, self-evolution approaches that enable LLMs to autonomously acquire, refine, and learn from experiences generated by the model itself are rapidly growing.
  arXiv  Detail & Related papers  (2024-04-22T17:43:23Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Solution-oriented Agent-based Models Generation with Verifier-assisted Iterative In-context Learning [10.67134969207797]
  Agent-based models (ABMs) stand as an essential paradigm for proposing and validating hypothetical solutions or policies. Large language models (LLMs) encapsulating cross-domain knowledge and programming proficiency could potentially alleviate the difficulty of this process. We present SAGE, a general solution-oriented ABM generation framework designed for automatic modeling and generating solutions for targeted problems.
  arXiv  Detail & Related papers  (2024-02-04T07:59:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.