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 2551–2560 of 15113 papers

TitleStatusHype
Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning—0
On-Policy Optimization with Group Equivalent Preference for Multi-Programming Language Understanding—0
Counterfactual Explanations for Continuous Action Reinforcement LearningCode0
Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs—0
Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach—0
DGRO: Enhancing LLM Reasoning via Exploration-Exploitation Control and Reward Variance Management—0
Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning—0
ToTRL: Unlock LLM Tree-of-Thoughts Reasoning Potential through Puzzles Solving—0
Benchmarking MOEAs for solving continuous multi-objective RL problemsCode0
J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization—0
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Benchmark Results

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