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Forget hand-tuning transfer learning hyperparameters: $Q$Avatar adaptively combines source and target domain Q-functions for reliable cross-domain RL without them.
Data valuation for LLMs doesn't need backpropagation: a single forward pass is enough to match gradient-based methods, unlocking massive speedups.
