1 citations · 3 across the 6 of their papers we have counts for
6 papers
Stable-Layers: Fine-Tuning Image Layer Decomposition Models with VLM-Scored Reinforcement Learning
Ciara Rowles, Reshinth Adithyan, Nikhil Pinnaparaju +2
We present Stable-Layers, a reinforcement learning framework that eliminates the need for paired supervision by fine-tuning a pretrained layer decomposition model using only feedba…
Results of the NeurIPS 2023 Neural MMO Competition on Multi-task Reinforcement Learning
Joseph Suárez, Kyoung Whan Choe, David Bloomin +22
We present the results of the NeurIPS 2023 Neural MMO Competition, which attracted over 200 participants and submissions. Participants trained goal-conditional policies that genera…
Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic
Zaid Alyafeai, Michael Pieler, Hannah Teufel +8
Large Language Models (LLMs) have shown impressive results in multiple domains of natural language processing (NLP) but are mainly focused on the English language. Recently, more L…
Rephrasing natural text data with different languages and quality levels for Large Language Model pre-training
Michael Pieler, Marco Bellagente, Hannah Teufel +9
Recently published work on rephrasing natural text data for pre-training LLMs has shown promising results when combining the original dataset with the synthetically rephrased data.…
Stable Code Technical Report
Nikhil Pinnaparaju, Reshinth Adithyan, Duy Phung +8
We introduce Stable Code, the first in our new-generation of code language models series, which serves as a general-purpose base code language model targeting code completion, reas…
Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning
Joseph Suárez, Phillip Isola, Kyoung Whan Choe +15
Neural MMO 2.0 is a massively multi-agent environment for reinforcement learning research. The key feature of this new version is a flexible task system that allows users to define…