San Mateo
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RUDDER: Return Decomposition for Delayed Rewards
Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, Sepp Hochreiter
Neural Information Processing Systems http://nips.cc/
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On Reinforcement Learningand Distribution Matchingfor Fine-Tuning Language Models withno Catastrophic Forgetting
Twoofthemcanbecharacterizedas "Reward Maximization" (RM): Standard Policy Gradients (PG) and KL-control.
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Fast Rank-1 Lattice Targeted Sampling for Black-box Optimization
Müller et al. [2021], Nguyen et al. [2022] builds a GP model for both the function value and the
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