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 2276–2300 of 15113 papers

TitleStatusHype
Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across ModalitiesCode0
High-Throughput Distributed Reinforcement Learning via Adaptive Policy SynchronizationCode0
Local Pairwise Distance Matching for Backpropagation-Free Reinforcement Learning—0
The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs—0
Scaling RL to Long Videos—0
Video-RTS: Rethinking Reinforcement Learning and Test-Time Scaling for Efficient and Enhanced Video Reasoning—0
Squeeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model—0
Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation—0
Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study—0
CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation—0
FEVO: Financial Knowledge Expansion and Reasoning Evolution for Large Language Models—0
Robust Bandwidth Estimation for Real-Time Communication with Offline Reinforcement Learning—0
2048: Reinforcement Learning in a Delayed Reward Environment—0
Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for Visual Reasoning—0
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across DomainsCode0
Constructing Non-Markovian Decision Process via History AggregatorCode0
Listener-Rewarded Thinking in VLMs for Image Preferences—0
A Survey of Continual Reinforcement Learning—0
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review—0
Homogenization of Multi-agent Learning Dynamics in Finite-state Markov GamesCode0
RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment—0
Optimising 4th-Order Runge-Kutta Methods: A Dynamic Heuristic Approach for Efficiency and Low Storage—0
Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning—0
Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning—0
Curriculum-Guided Antifragile Reinforcement Learning for Secure UAV Deconfliction under Observation-Space Attacks—0
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Benchmark Results

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