Search Results for author:
Found 11 papers, 6 papers with code
A distributional view on multi objective policy optimization
Many real-world problems require trading off multiple competing objectives.
Competitive Programming with Large Reasoning Models
We competed live at IOI 2024 with o1-ioi and, using hand-crafted test-time strategies, placed in the 49th percentile.
OpenAI o1 System Card
The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought.
Solving math word problems with process- and outcome-based feedback
Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks.
Ranked #33 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Teaching language models to support answers with verified quotes
We measure the performance of GopherCite by conducting human evaluation of answers to questions in a subset of the NaturalQuestions and ELI5 datasets.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Ranked #1 on
Language Modelling
on StackExchange
Synthetic Returns for Long-Term Credit Assignment
We propose state-associative (SA) learning, where the agent learns associations between states and arbitrarily distant future rewards, then propagates credit directly between the two.
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents
There has been rapidly growing interest in meta-learning as a method for increasing the flexibility and sample efficiency of reinforcement learning.
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
We present the Bayesian action decoder (BAD), a new multi-agent learning method that uses an approximate Bayesian update to obtain a public belief that conditions on the actions taken by all agents in the environment.
Relational inductive biases, deep learning, and graph networks
As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
