Understanding Reasoning LLMs - by Sebastian Raschka, PhD

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语言： 英语

关键字： Reasoning Models, LLMs, Reinforcement Learning, Supervised Finetuning, Distillation

概述： This article provides a comprehensive overview of reasoning models, a specialized area within the LLM field focused on enhancing LLMs for complex tasks requiring multi-step problem-solving. It defines reasoning models, discusses their advantages and disadvantages, and outlines four main approaches to building and improving them: inference-time scaling, pure reinforcement learning (RL), supervised finetuning (SFT) and reinforcement learning (RL), and pure supervised finetuning (SFT) and distillation. The article also touches on the DeepSeek R1 pipeline as a case study and offers practical advice for developing reasoning models on a limited budget. The author emphasizes the importance of choosing the right type of LLM for the task and highlights the potential of combining different techniques to achieve optimal performance.

分节阅读：

• How do we define “reasoning model”?

  • Reasoning is defined as answering questions requiring complex, multi-step generation.
  • Reasoning models excel at complex tasks like puzzles and mathematical proofs.
  • These models often include a “thought” process as part of their response.

• When should we use reasoning models?

  • Reasoning models are designed for complex tasks like solving puzzles and advanced math problems.
  • They are not necessary for simpler tasks like summarization or translation.
  • Using reasoning models for everything can be inefficient and expensive.

• A brief look at the DeepSeek training pipeline

  • DeepSeek developed three distinct variants: DeepSeek-R1-Zero, DeepSeek-R1, and DeepSeek-R1-Distill.
  • DeepSeek-R1-Zero was trained using reinforcement learning (RL) without a supervised fine-tuning (SFT) step.
  • DeepSeek-R1 was further refined with additional SFT stages and further RL training.

• The 4 main ways to build and improve reasoning models

  • Inference-time scaling improves reasoning capabilities by increasing computational resources during inference.
  • Pure reinforcement learning (RL) can lead to the emergence of reasoning as a behavior.
  • Supervised fine-tuning (SFT) plus RL is a common approach for building high-performance reasoning models.
  • Model distillation involves training smaller models on data generated by larger LLMs.

• Thoughts about DeepSeek R1

  • The DeepSeek-R1 models are an awesome achievement.
  • DeepSeek-R1 is more efficient at inference time compared to o1.
  • It is difficult to compare o1 and DeepSeek-R1 directly because OpenAI has not disclosed much about o1.

• Developing reasoning models on a limited budget

  • Model distillation offers a more cost-effective alternative.
  • Smaller, targeted fine-tuning efforts can still yield impressive results at a fraction of the cost.
  • Journey learning includes incorrect solution paths, allowing the model to learn from mistakes.

相关工具：

• LeetCode compiler: (No direct link provided in the article, use general search)
• TinyZero: https://github.com/Jiayi-Pan/TinyZero/

参考文献：

• DeepSeek R1 technical report: https://arxiv.org/abs/2501.12948
• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters: https://arxiv.org/abs/2408.03314
• Large Language Models are Zero-Shot Reasoners: https://arxiv.org/abs/2205.11916
• LLM Training: RLHF and Its Alternatives: https://magazine.sebastianraschka.com/p/llm-training-rlhf-and-its-alternatives
• Sky-T1: Train your own O1 preview model within $450: https://novasky-ai.github.io/posts/sky-t1/
• O1 Replication Journey: A Strategic Progress Report – Part 1: https://arxiv.org/abs/2410.18982

原文链接： https://magazine.sebastianraschka.com/p/understanding-reasoning-llms

source: https://magazine.sebastianraschka.com/p/understanding-reasoning-llms