From Large Language Models to Large Reasoning Models: Focus on Reinforcement Learning

The rapid development of large language models (LLMs) has revolutionized natural language processing. While LLMs demonstrate impressive capabilities in text generation and analysis, they often reach their limits with complex reasoning tasks. A promising approach to overcome these limitations lies in reinforcement learning (RL), which enables LLMs to learn and optimize reasoning processes.

The Concept of "Thought"

A central aspect of this research is the introduction of the concept of "thought" within LLMs. This refers to a sequence of tokens that represents intermediate steps in the reasoning process. Similar to how humans formulate intermediate solutions or subgoals, LLMs can process complex tasks step-by-step through these "thoughts." This allows them to imitate human reasoning processes like tree search or reflective thinking.

Reinforcement Learning as the Key to Success

Through the use of RL, LLMs can learn to generate optimal "thought" sequences. The learning process is based on a trial-and-error principle, where the model is rewarded for successful reasoning steps and penalized for missteps. This approach enables the automatic generation of high-quality reasoning paths and significantly expands the reasoning ability of LLMs by providing significantly more training data.

Scaling in Training and Inference

Current research findings show that scaling during both training and inference, i.e., the application of the model, is crucial for the performance of reasoning models. In training, a larger amount of data and longer training times allow the model to capture more complex relationships. In inference, using more "thought" tokens leads to higher accuracy in reasoning tasks. The combination of these two scaling approaches paves the way for so-called "large reasoning models."

Open-Source Projects and Future Challenges

The development of large reasoning models is an active research field, and there are already a number of open-source projects that deal with this topic. Despite the promising progress, there are still numerous challenges to overcome. These include the development of more efficient RL algorithms, improving the interpretability of "thought" sequences, and the development of robust evaluation metrics for reasoning models.

Conclusion

The combination of LLMs with reinforcement learning opens up new possibilities for the development of AI systems with improved reasoning capabilities. Scaling in training and inference, coupled with the concept of "thought," lays the foundation for the emergence of large reasoning models that can solve complex problems and replicate human-like thought processes. Further research in this area promises exciting developments and could fundamentally change the way we interact with AI.

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