Results of the summarizing process for the arXiv paper: 2501.09686v3

Language has long been recognized as a fundamental tool for human reasoning. The emergence of Large Language Models (LLMs) has sparked considerable interest in utilizing these models to tackle complex reasoning tasks. Researchers have advanced beyond simple autoregressive token generation by introducing the concept of "thought" - a sequence of tokens representing intermediate steps in the reasoning process. This innovative approach allows LLMs to emulate intricate human reasoning processes like tree search and reflective thinking. A recent trend in learning to reason involves applying reinforcement learning (RL) to train LLMs to excel in reasoning processes. This method enables the automatic generation of high-quality reasoning trajectories through trial-and-error search algorithms, significantly enhancing LLMs' reasoning capacity by providing ample training data. Moreover, studies have shown that encouraging LLMs to engage in more extensive "thinking" with additional tokens during test-time inference can substantially improve reasoning accuracy. The combination of train-time and test-time scaling presents a new research frontier towards developing Large Reasoning Models. The introduction of OpenAI's o1 series signifies a significant milestone in this research direction. In this survey, we delve into recent advancements in LLM reasoning, starting with an introduction to the foundational background of LLMs. We then explore key technical components driving the development of large reasoning models, such as automated data construction, learning-to-reason techniques, and test-time scaling. Furthermore, we discuss how test-time enhancing techniques have the potential to further enhance LLMs' reasoning capacities by enabling them to strategically reason across solution spaces, leverage past experiences, and dynamically optimize workflows. Additionally, designing robust evaluation benchmarks is crucial for documenting improvements in LLM capabilities and guiding future research directions. We review popular benchmarks for LLM reasoning and categorize them systematically based on their taxonomy. Overall, the field is moving towards developing Large Reasoning Models that can mimic complex human-like reasoning processes effectively. By leveraging advancements in automated data construction, learning-to-reason techniques, and test-time scaling strategies, researchers are paving the way for more sophisticated and capable language models that can excel at complex reasoning tasks.

• Language is a fundamental tool for human reasoning
• Large Language Models (LLMs) are being used for complex reasoning tasks
• Introduction of "thought" concept allows LLMs to emulate human reasoning processes
• Reinforcement learning (RL) is applied to train LLMs for better reasoning capabilities
• Encouraging LLMs to engage in more extensive "thinking" during test-time improves reasoning accuracy
• Train-time and test-time scaling lead to the development of Large Reasoning Models
• OpenAI's o1 series marks a significant milestone in this research direction
• Automated data construction, learning-to-reason techniques, and test-time scaling are key components driving large reasoning model development
• Test-time enhancing techniques can further enhance LLMs' reasoning capacities by enabling strategic reasoning across solution spaces, leveraging past experiences, and optimizing workflows dynamically
• Designing robust evaluation benchmarks is crucial for documenting improvements in LLM capabilities and guiding future research directions

Introduction:

Language has been a fundamental tool for human reasoning since the dawn of civilization. It allows us to communicate, express our thoughts and ideas, and make sense of the world around us. With the rise of Artificial Intelligence (AI) and Natural Language Processing (NLP), researchers have been exploring ways to utilize language as a means for complex reasoning tasks. This has led to the emergence of Large Language Models (LLMs), which have sparked considerable interest in the field.

The Concept of "Thought":

Traditionally, LLMs were used for simple autoregressive token generation, where they would generate tokens one at a time based on previous tokens in a sequence. However, this approach was limited in its ability to handle more complex reasoning tasks. To address this issue, researchers introduced the concept of "thought" - a sequence of tokens representing intermediate steps in the reasoning process.

By incorporating thought into LLMs, they can now emulate intricate human-like reasoning processes such as tree search and reflective thinking. This innovative approach has opened up new possibilities for using LLMs in various applications that require advanced reasoning abilities.

Reinforcement Learning (RL) for Training LLMs:

One recent trend in learning to reason involves applying reinforcement learning (RL) techniques to train LLMs. RL is an AI technique that enables machines to learn through trial-and-error interactions with their environment. By using RL algorithms, researchers can automatically generate high-quality reasoning trajectories that provide ample training data for LLMs.

This method has significantly enhanced LLMs' reasoning capacity by allowing them to learn from their mistakes and improve over time through continuous training. As a result, these models can now tackle more complex reasoning tasks with greater accuracy.

Test-Time Scaling:

Another crucial aspect of developing large reasoning models is test-time scaling - enhancing LLM performance during inference or testing phase by providing additional tokens or information related to the task at hand.

Studies have shown that encouraging LLMs to engage in more extensive "thinking" with additional tokens during test-time inference can substantially improve reasoning accuracy. This approach enables LLMs to strategically reason across solution spaces, leverage past experiences, and dynamically optimize workflows.

OpenAI's o1 Series:

The recent introduction of OpenAI's o1 series marks a significant milestone in the development of Large Reasoning Models. These models have been trained on an unprecedented amount of data and have achieved impressive results on various reasoning tasks.

In this survey, we delve into recent advancements in LLM reasoning, starting with an introduction to the foundational background of LLMs. We then explore key technical components driving the development of large reasoning models, such as automated data construction, learning-to-reason techniques, and test-time scaling.

Furthermore, we discuss how test-time enhancing techniques have the potential to further enhance LLMs' reasoning capacities by enabling them to strategically reason across solution spaces, leverage past experiences, and dynamically optimize workflows.

Evaluation Benchmarks for LLM Reasoning:

Designing robust evaluation benchmarks is crucial for documenting improvements in LLM capabilities and guiding future research directions. In this article, we review popular benchmarks for LLM reasoning and categorize them systematically based on their taxonomy.

Conclusion:

Overall, the field is moving towards developing Large Reasoning Models that can mimic complex human-like reasoning processes effectively. By leveraging advancements in automated data construction, learning-to-reason techniques, and test-time scaling strategies, researchers are paving the way for more sophisticated and capable language models that can excel at complex reasoning tasks. With continued research and development in this area, we can expect even more significant breakthroughs in utilizing language as a tool for human-like reasoning.