487 citations · 1.5k across the 14 of their papers we have counts for
14 papers
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet +1
We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We id…
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Petko Georgiev, Ving Ian Lei +1132
In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…
A Generalist Agent
Scott Reed, Konrad Zolna, Emilio Parisotto +17
Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we…
Launchpad: A Programming Model for Distributed Machine Learning Research
Fan Yang, Gabriel Barth-Maron, Piotr Stańczyk +5
A major driver behind the success of modern machine learning algorithms has been their ability to process ever-larger amounts of data. As a result, the use of distributed systems i…
Reverb: A Framework For Experience Replay
Albin Cassirer, Gabriel Barth-Maron, Eugene Brevdo +4
A central component of training in Reinforcement Learning (RL) is Experience: the data used for training. The mechanisms used to generate and consume this data have an important ef…
Acme: A Research Framework for Distributed Reinforcement Learning
Matthew W. Hoffman, Bobak Shahriari, John Aslanides +36
Deep reinforcement learning (RL) has led to many recent and groundbreaking advances. However, these advances have often come at the cost of both increased scale in the underlying a…
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning
Srivatsan Krishnan, Maximilian Lam, Sharad Chitlangia +4
Deep reinforcement learning continues to show tremendous potential in achieving task-level autonomy, however, its computational and energy demands remain prohibitively high. In thi…
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
Tom Le Paine, Caglar Gulcehre, Bobak Shahriari +11
This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial…
One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL
Tom Le Paine, Sergio Gómez Colmenarejo, Ziyu Wang +8
Humans are experts at high-fidelity imitation -- closely mimicking a demonstration, often in one attempt. Humans use this ability to quickly solve a task instance, and to bootstrap…
Observe and Look Further: Achieving Consistent Performance on Atari
Tobias Pohlen, Bilal Piot, Todd Hester +10
Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks…
Distributed Distributional Deterministic Policy Gradients
Gabriel Barth-Maron, Matthew W. Hoffman, David Budden +6
This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting. We combine this within a distributed fram…
Distributed Prioritized Experience Replay
Dan Horgan, John Quan, David Budden +4
We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible…
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation
Ivaylo Popov, Nicolas Heess, Timothy Lillicrap +7
Deep learning and reinforcement learning methods have recently been used to solve a variety of problems in continuous control domains. An obvious application of these techniques is…