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 381–390 of 15113 papers

TitleStatusHype
Benchmarking MOEAs for solving continuous multi-objective RL problemsCode0
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis—0
On-Policy Optimization with Group Equivalent Preference for Multi-Programming Language Understanding—0
ToTRL: Unlock LLM Tree-of-Thoughts Reasoning Potential through Puzzles Solving—0
Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision TraceabilityCode1
G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement LearningCode2
Your Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimization—0
Counterfactual Explanations for Continuous Action Reinforcement LearningCode0
Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMsCode1
Optimizing Anytime Reasoning via Budget Relative Policy OptimizationCode2
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Benchmark Results

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