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 351–360 of 15113 papers

TitleStatusHype
Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectives—0
StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy OptimizationCode0
Learning-based Autonomous Oversteer Control and Collision Avoidance—0
RL Tango: Reinforcing Generator and Verifier Together for Language ReasoningCode2
Thought-Augmented Policy Optimization: Bridging External Guidance and Internal Capabilities—0
ViaRL: Adaptive Temporal Grounding via Visual Iterated Amplification Reinforcement Learning—0
Pass@K Policy Optimization: Solving Harder Reinforcement Learning Problems—0
GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI AgentsCode1
MMaDA: Multimodal Large Diffusion Language ModelsCode0
Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One—0
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Benchmark Results

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