8 papers
When Does Predictive Inverse Dynamics Outperform Behavior Cloning?
Lukas Schäfer, Pallavi Choudhury, Abdelhak Lemkhenter +10
Behavior cloning (BC) is a practical offline imitation learning method, but it often fails when expert demonstrations are limited. Recent works have introduced a class of architect…
Next-Latent Prediction Transformers Learn Compact World Models
Jayden Teoh, Manan Tomar, Kwangjun Ahn +7
Transformers replace recurrence with a memory that grows with sequence length and self-attention that enables ad-hoc lookups over past tokens. Consequently, they lack an inherent i…
SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning
Yao-Hui Li, Zeyu Wang, Xin Li +7
Model-based reinforcement learning (MBRL) is sample-efficient but struggles in sparse reward settings. A critical bottleneck arises from the lack of informative gradients in sparse…
Learning Additively Compositional Latent Actions for Embodied AI
Hangxing Wei, Xiaoyu Chen, Chuheng Zhang +5
Latent action learning infers pseudo-action labels from visual transitions, providing an approach to leverage internet-scale video for embodied AI. However, most methods learn late…
Next Concept Prediction in Discrete Latent Space Leads to Stronger Language Models
Yuliang Liu, Yunchong Song, Yixuan Wang +6
We propose Next Concept Prediction (NCP), a generative pretraining paradigm built on top of Next Token Prediction (NTP). NCP predicts discrete concepts that span multiple tokens, t…
The Belief State Transformer
Edward S. Hu, Kwangjun Ahn, Qinghua Liu +9
We introduce the "Belief State Transformer", a next-token predictor that takes both a prefix and suffix as inputs, with a novel objective of predicting both the next token for the…