18 citations · 57 across the 9 of their papers we have counts for
16 papers
Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer
Gemini Robotics Team, Abbas Abdolmaleki, Saminda Abeyruwan +169
General-purpose robots need a deep understanding of the physical world, advanced reasoning, and general and dexterous control. This report introduces the latest generation of the G…
Vision-Language Model Dialog Games for Self-Improvement
Ksenia Konyushkova, Christos Kaplanis, Serkan Cabi +1
The increasing demand for high-quality, diverse training data poses a significant bottleneck in advancing vision-language models (VLMs). This paper presents VLM Dialog Games, a nov…
Reinforced Self-Training (ReST) for Language Modeling
Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan +11
Reinforcement learning from human feedback (RLHF) can improve the quality of large language model's (LLM) outputs by aligning them with human preferences. We propose a simple algor…
: Policy Representations with Successor Features
Gianluca Scarpellini, Ksenia Konyushkova, Claudio Fantacci +3
This paper describes , a method for representing behaviors of black box policies as feature vectors. The policy representations capture how the statistics of founda…
Vision-Language Models as Success Detectors
Yuqing Du, Ksenia Konyushkova, Misha Denil +5
Detecting successful behaviour is crucial for training intelligent agents. As such, generalisable reward models are a prerequisite for agents that can learn to generalise their beh…
Retrieval-Augmented Reinforcement Learning
Anirudh Goyal, Abram L. Friesen, Andrea Banino +13
Most deep reinforcement learning (RL) algorithms distill experience into parametric behavior policies or value functions via gradient updates. While effective, this approach has se…