1 citations · 3 across the 5 of their papers we have counts for
7 papers
Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments
Romain Froger, Pierre Andrews, Matteo Bettini +21
We introduce Gaia2, a benchmark for evaluating large language model agents in realistic, asynchronous environments. Unlike prior static or synchronous evaluations, Gaia2 introduces…
The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes
Redacted by arXiv
This document consolidates publicly reported technical details about Metas Llama 4 model family. It summarizes (i) released variants (Scout and Maverick) and the broader herd conte…
ARE: Scaling Up Agent Environments and Evaluations
Romain Froger, Pierre Andrews, Matteo Bettini +21
We introduce Meta Agents Research Environments (ARE), a research platform for scalable creation of environments, integration of synthetic or real applications, and execution of age…
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…
WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language Models
Youssef Benchekroun, Megi Dervishi, Mark Ibrahim +7
We propose WorldSense, a benchmark designed to assess the extent to which LLMs are consistently able to sustain tacit world models, by testing how they draw simple inferences from…
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
Alexandre Ramé, Guillaume Couairon, Mustafa Shukor +4
Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further a…