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 26–50 of 15113 papers

TitleStatusHype
CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation—0
FEVO: Financial Knowledge Expansion and Reasoning Evolution for Large Language Models—0
GTA1: GUI Test-time Scaling AgentCode2
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
Robust Bandwidth Estimation for Real-Time Communication with Offline Reinforcement Learning—0
Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for Visual Reasoning—0
2048: Reinforcement Learning in a Delayed Reward Environment—0
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across DomainsCode0
Kwai Keye-VL Technical ReportCode4
RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query ParallelismCode5
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
Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement LearningCode2
APO: Enhancing Reasoning Ability of MLLMs via Asymmetric Policy OptimizationCode0
Strict Subgoal Execution: Reliable Long-Horizon Planning in Hierarchical Reinforcement Learning—0
Optimising 4th-Order Runge-Kutta Methods: A Dynamic Heuristic Approach for Efficiency and Low Storage—0
Robust Policy Switching for Antifragile Reinforcement Learning for UAV Deconfliction in Adversarial Environments—0
Curriculum-Guided Antifragile Reinforcement Learning for Secure UAV Deconfliction under Observation-Space Attacks—0
HumanOmniV2: From Understanding to Omni-Modal Reasoning with ContextCode2
Homogenization of Multi-agent Learning Dynamics in Finite-state Markov GamesCode0
RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment—0
Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning—0
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Benchmark Results

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