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 326–350 of 15113 papers

TitleStatusHype
Distilling the Implicit Multi-Branch Structure in LLMs' Reasoning via Reinforcement Learning—0
Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games—0
ARPO:End-to-End Policy Optimization for GUI Agents with Experience ReplayCode2
Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement LearningCode3
Meta-reinforcement learning with minimum attention—0
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)—0
SWE-Dev: Evaluating and Training Autonomous Feature-Driven Software DevelopmentCode2
Find the Fruit: Designing a Zero-Shot Sim2Real Deep RL Planner for Occlusion Aware Plant Manipulation—0
VL-SAFE: Vision-Language Guided Safety-Aware Reinforcement Learning with World Models for Autonomous Driving—0
Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only—0
Offline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies—0
Reward Is Enough: LLMs Are In-Context Reinforcement Learners—0
Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning—0
GRIT: Teaching MLLMs to Think with Images—0
RLBenchNet: The Right Network for the Right Reinforcement Learning TaskCode1
From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement LearningCode1
Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One—0
MMaDA: Multimodal Large Diffusion Language ModelsCode0
An Empirical Study on Reinforcement Learning for Reasoning-Search Interleaved LLM AgentsCode7
VARD: Efficient and Dense Fine-Tuning for Diffusion Models with Value-based RL—0
StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy OptimizationCode0
Thought-Augmented Policy Optimization: Bridging External Guidance and Internal Capabilities—0
ViaRL: Adaptive Temporal Grounding via Visual Iterated Amplification Reinforcement Learning—0
STAR-R1: Spacial TrAnsformation Reasoning by Reinforcing Multimodal LLMsCode0
Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectives—0
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Benchmark Results

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