activity
20232026
most citedScaling Laws for Pre-training Agents and World Models

2 citations · 3 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL2026

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…

cs.LG2026

Training AI Scientists to Replicate Research

Damon Falck, Samer Sabri, Anja Surina +8

The replicability of papers is a cornerstone of scientific knowledge, ensuring the reliability of existing results and providing a base for further experiments. The act of replicat…

cs.AI2026

Hyperagents

Jenny Zhang, Bingchen Zhao, Wannan Yang +5

Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improveme…

cs.LG2025

Adapting Vision-Language Models for Evaluating World Models

Mariya Hendriksen, Tabish Rashid, David Bignell +5

World models - generative models that simulate environment dynamics conditioned on past observations and actions - are gaining prominence in planning, simulation, and embodied AI.…

cs.LG2024★ 2 cited

Scaling Laws for Pre-training Agents and World Models

Tim Pearce, Tabish Rashid, Dave Bignell +3

The performance of embodied agents has been shown to improve by increasing model parameters, dataset size, and compute. This has been demonstrated in domains from robotics to video…

cs.LG2024

Efficient Offline Reinforcement Learning: First Imitate, then Improve

Adam Jelley, Trevor McInroe, Sam Devlin +1

Supervised imitation-based approaches are often favored over off-policy reinforcement learning approaches for learning policies offline, since their straightforward optimization ob…

cs.LG2024

Aligning Agents like Large Language Models

Adam Jelley, Yuhan Cao, Dave Bignell +3

Training agents to act competently in complex 3D environments from high-dimensional visual information is challenging. Reinforcement learning is conventionally used to train such a…

cs.LG2023★ 1 cited

Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games

Lukas Schäfer, Logan Jones, Anssi Kanervisto +7

Video games have served as useful benchmarks for the decision-making community, but going beyond Atari games towards modern games has been prohibitively expensive for the vast majo…