14 citations · 25 across the 8 of their papers we have counts for
10 papers
Muse Glimmer 30B: An On-Device Agentic Model
Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed +327
We introduce Muse Glimmer, an open-source 30B parameter causal language model with perception encoder, purpose-built for autonomous agentic tasks on consumer hardware. Muse Glimmer…
Muse Spark Safety & Preparedness Report
Cristina Menghini, Peter Ney, Hamza Kwisaba +117
Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framewo…
AIRA_2: Overcoming Bottlenecks in AI Research Agents
Karen Hambardzumyan, Nicolas Baldwin, Edan Toledo +22
Existing research has identified three structural performance bottlenecks in AI research agents: (1) synchronous single-GPU execution constrains sample throughput, limiting the ben…
AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents
Alisia Lupidi, Bhavul Gauri, Thomas Simon Foster +34
LLM agents hold significant promise for advancing scientific research. To accelerate this progress, we introduce AIRS-Bench (the AI Research Science Benchmark), a suite of 20 tasks…
A Scalable Measure of Loss Landscape Curvature for Analyzing the Training Dynamics of LLMs
Dayal Singh Kalra, Jean-Christophe Gagnon-Audet, Andrey Gromov +4
Understanding the curvature evolution of the loss landscape is fundamental to analyzing the training dynamics of neural networks. The most commonly studied measure, Hessian sharpne…
The Generalization Gap in Offline Reinforcement Learning
Ishita Mediratta, Qingfei You, Minqi Jiang +1
Despite recent progress in offline learning, these methods are still trained and tested on the same environment. In this paper, we compare the generalization abilities of widely us…
Understanding the Effects of RLHF on LLM Generalisation and Diversity
Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis +4
Large language models (LLMs) fine-tuned with reinforcement learning from human feedback (RLHF) have been used in some of the most widely deployed AI models to date, such as OpenAI'…
Stabilizing Unsupervised Environment Design with a Learned Adversary
Ishita Mediratta, Minqi Jiang, Jack Parker-Holder +3
A key challenge in training generally-capable agents is the design of training tasks that facilitate broad generalization and robustness to environment variations. This challenge m…
Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds
Ayush Jain, Nikolaos Gkanatsios, Ishita Mediratta +1
Most models tasked to ground referential utterances in 2D and 3D scenes learn to select the referred object from a pool of object proposals provided by a pre-trained detector. This…
CoCoNets: Continuous Contrastive 3D Scene Representations
Shamit Lal, Mihir Prabhudesai, Ishita Mediratta +2
This paper explores self-supervised learning of amodal 3D feature representations from RGB and RGB-D posed images and videos, agnostic to object and scene semantic content, and eva…