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

Showing 401410 of 15113 papers

TitleStatusHype
A Finite-Sample Analysis of Distributionally Robust Average-Reward Reinforcement Learning0
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios0
AbFlowNet: Optimizing Antibody-Antigen Binding Energy via Diffusion-GFlowNet Fusion0
Observe-R1: Unlocking Reasoning Abilities of MLLMs with Dynamic Progressive Reinforcement LearningCode0
VideoRFT: Incentivizing Video Reasoning Capability in MLLMs via Reinforced Fine-TuningCode2
Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement LearningCode3
UIShift: Enhancing VLM-based GUI Agents through Self-supervised Reinforcement Learning0
CPGD: Toward Stable Rule-based Reinforcement Learning for Language ModelsCode4
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise RewardCode3
AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning0
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Benchmark Results

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