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 341–350 of 15113 papers

TitleStatusHype
From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement LearningCode1
Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One—0
MMaDA: Multimodal Large Diffusion Language ModelsCode0
An Empirical Study on Reinforcement Learning for Reasoning-Search Interleaved LLM AgentsCode7
VARD: Efficient and Dense Fine-Tuning for Diffusion Models with Value-based RL—0
StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy OptimizationCode0
Thought-Augmented Policy Optimization: Bridging External Guidance and Internal Capabilities—0
ViaRL: Adaptive Temporal Grounding via Visual Iterated Amplification Reinforcement Learning—0
STAR-R1: Spacial TrAnsformation Reasoning by Reinforcing Multimodal LLMsCode0
Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectives—0
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Benchmark Results

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