Q* OpenAI

What is Q* (pronounced Q-star) from OpenAI? It is the AI breakthrough that will lead to AGI (Artificial general intelligence)? Did it cause the board to OpenAI to fire it's CEO Sam Altman?

Theory

Introduction

The recent developments in artificial intelligence, specifically in the realm of machine learning and deep learning, have brought forth a new concept: the Q* hypothesis. This idea revolves around the integration of tree-of-thoughts reasoning, process reward models, and the innovative use of synthetic data to enhance machine learning models.

Background

The Q* hypothesis, pronounced as Q-Star, is a concept that emerged from the artificial intelligence research community, particularly from OpenAI. The idea is a hybrid of various methodologies in machine learning and artificial intelligence, such as Q-learning, A* search algorithm, and others. Q* is seen as a potential breakthrough in the quest for Artificial General Intelligence (AGI), which aims to create autonomous systems that can outperform humans in most economically valuable tasks.

Tree-of-Thoughts Reasoning

Concept and Implementation

Tree-of-thoughts reasoning (ToT) is a novel approach in language model prompting. It involves the creation of a tree of reasoning paths, which may converge to a correct answer. This method is a significant step in advancing the capabilities of language models. By breaking down reasoning into chunks and prompting the model to generate new reasoning steps, ToT facilitates a more structured and efficient problem-solving process.

Comparison with Other Methods

ToT stands out from other problem-solving techniques with language models due to its recursive nature. This approach is akin to the concerns of AI Safety regarding recursively self-improving models. The ToT method scores each vertex or node in the reasoning tree, allowing for a more nuanced evaluation of the reasoning process. This technique aligns with the principles of Reinforcement Learning from Human Feedback (RLHF), as it allows for scoring of individual steps rather than entire completions.

Process Reward Models (PRM)

PRMs represent a critical shift in the way reinforcement learning and human feedback are utilized in language models. Traditional methods score the entire response from a language model, but PRMs assign a score to each step of reasoning. This fine-grained approach facilitates better understanding and optimization of language models. PRMs have been a topic of interest in the AI research community, with their application being essential in advancing the field of language model reasoning, particularly in complex tasks like mathematical problem-solving.

Supercharging Synthetic Data

The use of synthetic data in AI research has been gaining traction. This method involves creating large datasets through process supervision or similar techniques. Synthetic data is pivotal in training AI models, as it provides a vast and diverse range of scenarios for the models to learn from. The use of synthetic data, coupled with the advancements in ToT and PRM, paves the way for more sophisticated and capable AI systems.

Q* Hypothesis in Practice

The practical application of the Q* hypothesis involves using PRMs to score ToT reasoning data, which is then optimized with Offline Reinforcement Learning (RL). This methodology differs from traditional RLHF approaches by focusing on multi-step processes rather than single-step interactions. The intricacies of this method lie in the collection of appropriate prompts, generation of effective reasoning steps, and accurate scoring of a large number of completions.

Yann LeCun Response

Please ignore the deluge of complete nonsense about Q*.
One of the main challenges to improve LLM reliability is to replace Auto-Regressive token prediction with planning.

Pretty much every top lab (FAIR, DeepMind, OpenAl etc) is working on that and some have already published ideas and results.

It is likely that Q* is OpenAl attempts at planning. They pretty much hired Noam Brown (of Libratus/poker and Cicero/Diplomacy fame) to work on that.

[Note: I've been advocating for deep learning architecture capable of planning since 2016].