Search papers, labs, and topics across Lattice.
Affiliation:
4
0
4
0
QWM achieves unprecedented sample efficiency in reinforcement learning by leveraging world models without succumbing to compounding bias, outperforming prior methods on challenging benchmarks.
Naive pretraining of Q-functions may hinder performance, while a simple ensemble approach can lead to over 1.26x improvement in fine-tuning effectiveness.
Achieve near-perfect robotic manipulation with just 20 minutes of robot experience by smartly finetuning vision-language-action models with reinforcement learning.
Get the performance boost of expensive sampling-based RL policies for a fraction of the compute by learning to prune action candidates early in the diffusion denoising process.
