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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 571–580 of 15113 papers

TitleStatusHype
SwitchMT: An Adaptive Context Switching Methodology for Scalable Multi-Task Learning in Intelligent Autonomous Agents—0
Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning—0
Evolutionary Policy Optimization—0
NoisyRollout: Reinforcing Visual Reasoning with Data AugmentationCode2
LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard—0
Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement LearningCode2
TraCeS: Trajectory Based Credit Assignment From Sparse Safety Feedback—0
RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs—0
Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration—0
SkyReels-V2: Infinite-length Film Generative ModelCode9
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Benchmark Results

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