AI RESEARCH
Adaptive Replay Buffer for Offline-to-Online Reinforcement Learning
arXiv CS.LG
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ArXi:2512.10510v2 Announce Type: replace Offline-to-Online Reinforcement Learning (O2O RL) faces a critical dilemma in balancing the use of a fixed offline dataset with newly collected online experiences. Standard methods, often relying on a fixed data-mixing ratio, struggle to manage the trade-off between early learning stability and asymptotic performance. To overcome this, we