VIP learns a visual embedding from human videos whose distance defines dense, smooth rewards for arbitrary goal-image robot tasks without task-specific fine-tuning.
When should we prefer offline reinforcement learning over behavioral cloning?
4 Pith papers cite this work, alongside 17 external citations. Polarity classification is still indexing.
4
Pith papers citing it
17
external citations · external index
representative citing papers
Resampling clinical time series into uniform bins for offline RL reduces performance by up to 60% and causes retrospective evaluations to overestimate returns by 1.5-3x versus unprocessed data.
GLAN replaces CQL bootstrapping with Decision Transformer sequence modeling for PLPM, using global inter-day (L-RTG) and local session (HRM) modules to achieve +0.158% DAU and +0.108% LT gains in Kuaishou online tests.
Proposes causal reinforcement learning (CRL) as a framework that decomposes RL environments into structural causal models to unify online, off-policy, and causal learning while defining new tasks including generalized policy learning and counterfactual learning.
citing papers explorer
-
VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
VIP learns a visual embedding from human videos whose distance defines dense, smooth rewards for arbitrary goal-image robot tasks without task-specific fine-tuning.
-
The hidden risks of temporal resampling in clinical reinforcement learning
Resampling clinical time series into uniform bins for offline RL reduces performance by up to 60% and causes retrospective evaluations to overestimate returns by 1.5-3x versus unprocessed data.
-
From Bootstrapping to Sequence Modeling: A Unified Generative Framework for Personalized Landing-Page Modeling
GLAN replaces CQL bootstrapping with Decision Transformer sequence modeling for PLPM, using global inter-day (L-RTG) and local session (HRM) modules to achieve +0.158% DAU and +0.108% LT gains in Kuaishou online tests.
-
An Introduction to Causal Reinforcement Learning
Proposes causal reinforcement learning (CRL) as a framework that decomposes RL environments into structural causal models to unify online, off-policy, and causal learning while defining new tasks including generalized policy learning and counterfactual learning.