Uploaded July 2022 | Updated September 2026, 6 hours ago
Minerva is Google's new large language model (LLM) that can solve math questions. It is built on top of their PaLM model, with the biggest variant having 540 billion parameters. The paper uses these previous model, but with new math-specific data and analysis to test the current limits of LLMs when it comes to quantitative reasoning tasks. The results of the paper are impressive, but in several areas, still raises the question of what the next significant development in Machine Learning will be to push forward generalization.
----- Outline -----
It’s 5am and I need to sleep, will add tomorrow
----- Socials -----
YouTube: youtube.com/c/EdanMeyer
Twitter: twitter.com/ejmejm1
----- References -----
Paper (Solving Quantitative Reasoning Problems with Language Models): arxiv.org/abs/2206.14858
Blog Post: ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html
Minerva is Google's new large language model (LLM) that can solve math questions. It is built on top of their PaLM model, with the biggest variant having 540 billion parameters. The paper uses these previous model, but with new math-specific data and analysis to test the current limits of LLMs when it comes to quantitative reasoning tasks. The results of the paper are impressive, but in several areas, still raises the question of what the next significant development in Machine Learning will be to push forward generalization.
----- Outline -----
It’s 5am and I need to sleep, will add tomorrow
----- Socials -----
YouTube: youtube.com/c/EdanMeyer
Twitter: twitter.com/ejmejm1
----- References -----
Paper (Solving Quantitative Reasoning Problems with Language Models): arxiv.org/abs/2206.14858
Blog Post: ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html
![ML Research Idea [Zero to Paper]](https://i.ytimg.com/vi/nSEb6w_BBqE/mqdefault.jpg "ML Research Idea [Zero to Paper]
This episode (part 2) of Zero to Paper covers the idea we will be working on: Text to Goal, or TTG for short. TTG aims to translate natural language into a reward function for a Reinforcement Learning problem. This research project aims to leverage recent advancements in NLP and Computer Vision to make reward functions easier and more natural to craft.
I also briefly touch on how I come up with ideas an what is important to me. I hope this will be helpful to anyone wonder how to do ML research!
Zero to Paper playlist: https://www.youtube.com/watch?v=74fKCvr5n5o&list=PL_49VD9KwQ_ONxENRk11jFEI3_pqAwaug
RL Theory series if you want to get into RL: https://www.youtube.com/watch?v=1OI0uuz9jkI&list=PL_49VD9KwQ_OML1Knh-Yb7FUFkhTLS0jL")
