tl;dr: OpenAI leaked AI breakthrough called Q*, acing grade-school math. It is hypothesized combination of Q-learning and A*. It was then refuted. DeepMind is working on something similar with Gemini, AlphaGo-style Monte Carlo Tree Search. Scaling these might be crux of planning for increasingly abstract goals and agentic behavior. Academic community has been circling around these ideas for a while.

Reuters: OpenAI researchers warned board of AI breakthrough ahead of CEO ouster, sources say

Michael Trazzi, on Twitter:

"Ahead of OpenAI CEO Sam Altman’s four days in exile, several staff researchers sent the board of directors a letter warning of a powerful artificial intelligence discovery that they said could threaten humanity
Mira Murati told employees on Wednesday that a letter about the AI breakthrough called Q* (pronounced Q-Star), precipitated the board's actions.
Given vast computing resources, the new model was able to solve certain mathematical problems. Though only performing math on the level of grade-school students, acing such tests made researchers very optimistic about Q*’s future success."

Silas Alberti, Twitter:

"What could OpenAI’s breakthrough Q* be about?
It sounds like it’s related to Q-learning. (For example, Q* denotes the optimal solution of the Bellman equation.) Alternatively, referring to a combination of the A* algorithm and Q learning.
One natural guess is that it is AlphaGo-style Monte Carlo Tree Search of the token trajectory. 🔎 It seems like a natural next step: Previously, papers like AlphaCode showed that even very naive brute force sampling in an LLM can get you huge improvements in competitive programming. The next logical step is to search the token tree in a more principled way. This particularly makes sense in settings like coding and math where there is an easy way to determine correctness. -> Indeed, Q* seems to be about solving Math problems 🧮"

Mark Riedl, Twitter:

"Anyone want to speculate on OpenAI’s secret Q* project?
- Something similar to tree-of-thought with intermediate evaluation (like A*)?
- Monte-Carlo Tree Search like forward roll-outs with LLM decoder and q-learning (like AlphaGo)?
- Maybe they meant Q-Bert, which combines LLMs and deep Q-learning
Before we get too excited, the academic community has been circling around these ideas for a while. There are a ton of papers in the last 6 months that could be

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