SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 601–610 of 15113 papers

TitleStatusHype
DUMP: Automated Distribution-Level Curriculum Learning for RL-based LLM Post-trainingCode1
Adaptive Insurance Reserving with CVaR-Constrained Reinforcement Learning under Macroeconomic Regimes—0
Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making—0
Development of a PPO-Reinforcement Learned Walking Tripedal Soft-Legged Robot using SOFACode0
Towards Optimal Differentially Private Regret Bounds in Linear MDPs—0
Efficient Implementation of Reinforcement Learning over Homomorphic Encryption—0
Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and ChallengesCode0
Spectral Normalization for Lipschitz-Constrained Policies on Learning Humanoid Locomotion—0
Deep Distributional Learning with Non-crossing Quantile Network—0
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language ModelsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified