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 2526–2550 of 15113 papers

TitleStatusHype
Learning-based Autonomous Oversteer Control and Collision Avoidance—0
Guided Policy Optimization under Partial ObservabilityCode0
Chain-of-Focus: Adaptive Visual Search and Zooming for Multimodal Reasoning via RL—0
Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning—0
Reward Is Enough: LLMs Are In-Context Reinforcement Learners—0
Pass@K Policy Optimization: Solving Harder Reinforcement Learning Problems—0
StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy OptimizationCode0
Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning—0
VARD: Efficient and Dense Fine-Tuning for Diffusion Models with Value-based RL—0
STAR-R1: Spacial TrAnsformation Reasoning by Reinforcing Multimodal LLMsCode0
KIPPO: Koopman-Inspired Proximal Policy Optimization—0
NavBench: A Unified Robotics Benchmark for Reinforcement Learning-Based Autonomous Navigation—0
AAPO: Enhance the Reasoning Capabilities of LLMs with Advantage Momentum—0
Normalized Cut with Reinforcement Learning in Constrained Action Space—0
APEX: Empowering LLMs with Physics-Based Task Planning for Real-time InsightCode0
Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning—0
Think-J: Learning to Think for Generative LLM-as-a-JudgeCode0
Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models—0
Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks—0
RL of Thoughts: Navigating LLM Reasoning with Inference-time Reinforcement Learning—0
UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning—0
Bellman operator convergence enhancements in reinforcement learning algorithms—0
Self-Evolving Curriculum for LLM Reasoning—0
Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning—0
ToTRL: Unlock LLM Tree-of-Thoughts Reasoning Potential through Puzzles Solving—0
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Benchmark Results

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