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cs.LG2026
MaD-RL: Matching Distributions for Calibrating LLMs with Reinforcement Learning
Sourabh Kulkarni, Ksheeraj Sai Vepuri, Basar Demir +8
Reinforcement learning (RL) is widely used in language-model post-training to maximize rewards assigned to individual model outputs, such as scores from binary verifiers or reward…
cs.LG2022★ 1 cited
Regret Bounds for Risk-Sensitive Reinforcement Learning
O. Bastani, Y. J. Ma, E. Shen +1
In safety-critical applications of reinforcement learning such as healthcare and robotics, it is often desirable to optimize risk-sensitive objectives that account for tail outcome…