740: Q*: OpenAI's Rumored AGI Breakthrough — with @JonKrohnLearns

Techniques

OpenAI's QStar model employs innovative techniques to tackle complex problems. Jon Krohn explains that the model uses a method where a large language model (LLM) generates multiple solutions at each step, while a second model acts as a verifier to select the best one 1. This approach is inspired by the tree of thoughts concept, allowing the AI to explore various reasoning chains, not just linear ones.

This tree of thoughts approach allows an LLM to explore different reasoning chains branching off in various directions, not just one, not just linear.

--- Jon Krohn

The QStar model also incorporates deep Q learning, a technique that enables machines to excel at tasks like playing Atari video games by simulating thousands of scenarios and evaluating the best moves 2.

   

Problem Solving

QStar's problem-solving capabilities extend to real-world scenarios, showcasing its potential. Jon Krohn notes that QStar can solve complex math problems expressed in natural language, such as calculating the number of apples left after a series of transactions 3. This ability indicates a significant leap forward in AI capabilities.

If Q can solve relatively complex problems like this, math word, problems like this, that indicates a significant leap forward in AI capabilities.

--- Jon Krohn

Additionally, the model's use of the tree of thoughts approach allows it to tackle NP-hard problems, which require exploring many possible solutions and backtracking when necessary 1.