Generative Support Realignment for Cross-Domain Offline Reinforcement Learning
Quick summary
arXiv:2605.13054v2 Announce Type: replace-cross Abstract: Cross-domain offline reinforcement learning learns a target policy from pre-collected source and target datasets with different dynamics. When target data are scarce, effectively compensating for their limited coverage using source data remains challenging due to the discrepancy between domains. We propose Target-aligned Coverage Expansion (TCE), which leverages source states to generatively realign and expand the limited target support while controlling the generation error induced by this expansion. We further derive a performance gap
Key takeaways
- arXiv:2605.13054v2 Announce Type: replace-cross Abstract: Cross-domain offline reinforcement learning learns a target policy from pre-collected source and target datasets with different dynamics.
- When target data are scarce, effectively compensating for their limited coverage using source data remains challenging due to the discrepancy between domains.
- We propose Target-aligned Coverage Expansion (TCE), which leverages source states to generatively realign and expand the limited target support while controlling the generation error induced by this expansion.
Why it matters
“Generative Support Realignment for Cross-Domain Offline Reinforcement Learning” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
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