Primal-dual policy gradient algorithms achieve global non-asymptotic convergence for safe RLHF cast as infinite-horizon discounted CMDPs without fitting reward models.
Tengyang Xie, Dylan J
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.LG 3years
2026 3representative citing papers
OPRIDE improves query efficiency in offline PbRL via a principled in-dataset exploration strategy and discount scheduling, outperforming prior methods with fewer queries and providing theoretical guarantees.
Optimistic regression algorithms with Gibbs updates achieve high-probability KL-regret that degrades gracefully under pointwise KL misspecification for bandits and stagewise KL Bellman misspecification for episodic RL.
citing papers explorer
-
Policy Gradient Primal-Dual Method for Safe Reinforcement Learning from Human Feedback
Primal-dual policy gradient algorithms achieve global non-asymptotic convergence for safe RLHF cast as infinite-horizon discounted CMDPs without fitting reward models.
-
OPRIDE: Offline Preference-based Reinforcement Learning via In-Dataset Exploration
OPRIDE improves query efficiency in offline PbRL via a principled in-dataset exploration strategy and discount scheduling, outperforming prior methods with fewer queries and providing theoretical guarantees.
-
Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification
Optimistic regression algorithms with Gibbs updates achieve high-probability KL-regret that degrades gracefully under pointwise KL misspecification for bandits and stagewise KL Bellman misspecification for episodic RL.