ME-AM adds mirror-descent entropy maximization and a mixture behavior prior to adjoint matching in flow-based policies to mitigate popularity bias and support binding in offline RL.
[2025], provided here to ensure the paper remains self-contained
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Entropy-Regularized Adjoint Matching for Offline Reinforcement Learning
ME-AM adds mirror-descent entropy maximization and a mixture behavior prior to adjoint matching in flow-based policies to mitigate popularity bias and support binding in offline RL.