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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–175 of 15113 papers

TitleStatusHype
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
Learning to Clarify by Reinforcement Learning Through Reward-Weighted Fine-Tuning—0
Reliable Critics: Monotonic Improvement and Convergence Guarantees for Reinforcement Learning—0
Safety-Aware Reinforcement Learning for Control via Risk-Sensitive Action-Value Iteration and Quantile Regression—0
CARoL: Context-aware Adaptation for Robot Learning—0
On the Generalization of Data-Assisted Control in port-Hamiltonian Systems (DAC-pH)—0
QForce-RL: Quantized FPGA-Optimized Reinforcement Learning Compute Engine—0
Prompting Wireless Networks: Reinforced In-Context Learning for Power Control—0
Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement LearningCode0
Towards Infant Sleep-Optimized Driving: Synergizing Wearable and Vehicle Sensing in Intelligent Cruise Control—0
CodeContests+: High-Quality Test Case Generation for Competitive Programming—0
Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models—0
Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout ReplayCode1
Safe Planning and Policy Optimization via World Model Learning—0
Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning—0
Dissecting Long Reasoning Models: An Empirical StudyCode0
On the Mechanism of Reasoning Pattern Selection in Reinforcement Learning for Language Models—0
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning—0
Latent Guided Sampling for Combinatorial OptimizationCode0
Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning—0
A Lyapunov Drift-Plus-Penalty Method Tailored for Reinforcement Learning with Queue Stability—0
SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL—0
Learning-at-Criticality in Large Language Models for Quantum Field Theory and Beyond—0
CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design—0
Joint Modeling for Learning Decision-Making Dynamics in Behavioral Experiments—0
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Benchmark Results

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