Have you ever thought about how Artificial Intelligence (AI) deals with complicated problems or the mechanism behind its reasoning? Every day, we perform many operations that require reasoning through artificial intelligence. So how do these algorithms perform the operations we ask of them?
When it comes to addressing one of the most impressive outcomes in the area of artificial intelligence, perhaps it can be said that it is the advancement of reasoning abilities for solving complex problems and making human-like decisions.
Improvements in artificial intelligence have been growing exponentially, considering how large language models (LLMs) handle complex problems. Thanks to the chain-of-thought (CoT) mechanism, LLMs can do logical reasoning by breaking the multi-layered problems into smaller parts. That is, LLMs solve complex problems step by step. These mechanisms necessitate large loads of elaborate data labeled by humans to function. However, this kind of data is usually difficult to obtain.
In the research named “RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner”, Self-Taught Reasoner (STaR) was proposed to eliminate this problem. This framework aids models regarding reasoning without human interference by leveraging reinforcement learning (RL). STaR enables LLMs to produce their own reasoning sequence, and just those steps resulting in correct outcome are kept for future training. With each iteration, the model gets smarter and provides better results in time.
Before looking at the contributions of STaR framework, it might be useful to introduce the concept of reinforcement learning (RL).
Reinforcement learning refers to a process in which algorithms are taught to perform better decision-making to get the most optimum results in time. It resembles what humans do in a trial-and-error process—avoiding punishments and obtaining rewards. Like a toddler who learns to avoid fire or sharp objects after getting injured. Over a number of attempts, we display some effective behaviors and eliminate dysfunctional and incompetent ones.
Main contributions of the research
- The paper by Chang et al. (2024) shows STaR can theoretically provide the best reasoning in an infinite number of repetitions. This implies that STaR can learn how to effectively achieve the most accurate output, highlighting its high potential in practice.
- Also, how STaR uses flawed steps was analyzed. The model selects the steps that lead to the correct answer in each iteration but also includes misleading steps in the process. Since STaR concentrates on the steps that lead to the final correct outcome, misleading steps do not significantly affect the overall accuracy of the structure. On the contrary, it increases the rate at which the model reaches the correct result in complex problems.
- Finally, the research emphasizes the importance of high-quality pre-trained models as a base for STaR to function effectively. A certain level of accuracy in reasoning must be achieved for any pre-trained model to be able to use the STaR framework. If sufficient accuracy is not achieved at the beginning, the potential to achieve the expected progress decreases.
Limitations
Besides its advanced contributions, the model has several limitations. One of these is that the STaR framework relates each step only to the step before it, whereas in the real-world case, all steps in the process are considered. Another limitation is that it assumes there is just one path to reach the final correct outcome. This reduces flexibility. However, it is normally possible to follow more than one path to solve a problem. To illustrate, when dealing with a mathematical problem, several methods can be used to reach a result. These limitations reveal the model’s gaps with real-life scenarios, but they also pave the way for future studies.
Takeaways
Developments in AI continue unabated. LLM models can perform functions that were unimaginable before. A few years ago, many people were not even aware of such models, but today they have become an inseparable part of human life. We now do many of our daily tasks with the help of artificial intelligence, starting from making lists, calculations, and asking for suggestions on certain topics to music, movie, and series recommendations. What is more, we can even chat with systems like ChatGpt.
While some people find it very interesting that these models have a reasoning ability similar to humans, it is a fact that worries others. Moreover, it is difficult for us to predict what we will face over time. It brings many questions to our minds, such as whether artificial intelligence has a similar thinking structure to humans and whether it will have human-specific abilities such as emotions in the future. Given the rapid pace of developments, the time when we will have answers to these questions does not appear to be far off.
Reference article
Chang, F.-C., Lee, Y.-T., Shih, H.-Y., & Wu, P.-Y. (2024, October 31). RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner. Arxiv. https://arxiv.org/pdf/2410.23912
