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Interested in stabilizing the training your MAML? Do you want to substantially improve the generalization of MAML whilst cutting down hyperparameter tuning by learning most of the hyperparms automatically? Look no furthr Paper arxiv.org/abs/1810.09502 Code: github.com/AntreasAntonio…

A a very p(r)uny blogpost on Prunning Neural Networks. bayeswatch.com/2018/10/26/pru…

Random Network Distillation: A prediction-based method that achieves state-of-the-art performance on Montezuma’s Revenge -blog.openai.com/reinforcement-…

Nice article by James Vincent on OpenAI's new exploration + RL results. theverge.com/2018/11/1/1805…

"Dilated DenseNets for Relational Reasoning," Antoniou et al.: arxiv.org/abs/1811.00410

We’ve built an energy-based model that can quickly recognize, generate, and transfer simple concepts after only 5 training demos: blog.openai.com/learning-conce…

We did a thing!!

My latest blog post on meta-learning in general and "How to train your MAML" in particular is now out. bayeswatch.com/2018/11/30/HTY… The post thoroughly explains MAML, some of its problems, and proposes some solutions. In addition visualizes the learned per-step per layer learning rate

We’re releasing CoinRun, an environment generator that provides a metric for an agent’s ability to generalize across new environments - blog.openai.com/quantifying-ge…

Excited to share the release of Obstacle Tower! Inspired by Montezuma's Revenge, we've built it to act as a benchmark for hard problems in DeepRL: requiring vision, control, planning, and (importantly) generalization in order for agents to perform well. github.com/Unity-Technolo…

RL agents get specific to tasks they are trained on. What if we remove the task itself during training? Turns out, a self-supervised planning agent can both explore efficiently & achieve SOTA on test tasks w/ zero or few samples in DMControl from images! ramanans1.github.io/plan2explore

Posted my first paper on arXiv💥🙌 GPT-f is a Transformer-based automated theorem prover. We show that Transformer + Search is suitable to formal reasoning and continuous self-improvement 🦾 arxiv.org/abs/2009.03393

📔 New MiniF2F paper! arxiv.org/abs/2109.00110 Introduces MiniF2F a benchmark of Olympiad-level problem statements formalized in Lean/Metamath/Isabelle. GPT-f applied to MiniF2F/Metamath ~ 2% 🥶 GPT-f applied to MiniF2F/Lean ~ 29% 🔥 Code: github.com/openai/miniF2F 👇

When I started this project 2 years ago I couldn't have dreamt of us getting that far. But this is also only the beginning💥 Some thoughts on what we achieved so far 🧵

I'm in awe of the great work my teammates have done.

In September 2020 Quanta Magazine wrote about researchers trying to build an AI system that can achieve a gold-medal score at the IMO. New work by Stanislas Polu and co. at OpenAI takes another step in that direction. openai.com/blog/formal-ma…

AI proves formal math theorems🧮This paper uses language models for theorem proving, and expert iteration to automatically build a curriculum towards ever harder statements. The final system solves two(!) IMO problems. Watch the paper review here: youtu.be/lvYVuOmUVs8

I think the OpenAI board should resign

Final superalignment paper! We define a multi-agent game; get LLMs to play it and show that it makes their reasoning more “legible” to humans 1/n