First, let’s cover the basics:
Chain of Thoughts Defined: The Chain of Thoughts approach involves LLM AI generating step-by-step rationales to answer questions. By following this sequential reasoning process, the AI can improve its accuracy over time. The AI fine-tunes itself by analyzing the steps that led to correct answers, thereby refining its reasoning capabilities.
STaR Defined: STaR, or “Self-Taught Reasoner”, is a technique developed by researchers at Stanford University. It involves the AI iteratively generating rationales for answers and learning from its reasoning process. If an answer is incorrect, the model refines its approach, continually improving until it arrives at the correct solution. This method allows the AI to create its own training data and become increasingly intelligent over time.
Q-Learning Defined: Q-learning is a model-free reinforcement learning algorithm that determines the value of an action in a particular state. For instance, consider deciding whether to board a train to avoid traffic or drive a car to skip the wait of the train. The reward is calculated by finding the optimal time to reach your destination, factoring in travel time, traffic, waiting time, and costs. The model evaluates all options and selects the best one to achieve the final goal.
A* Defined: A* is a search algorithm that aims to find the path to a goal node with the smallest cost, such as the shortest distance or least time. It achieves this by maintaining a tree structure to evaluate possible paths and choosing the most efficient one.
So, What Is “Strawberry”?
“Strawberry” is likely a combination of Q STaR (Q*) and A* algorithms. This project could significantly enhance the reasoning abilities of AI models. However, the exact details remain confidential for now.
