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 2451–2500 of 15113 papers

TitleStatusHype
Rendering-Aware Reinforcement Learning for Vector Graphics Generation—0
Breaking the Performance Ceiling in Complex Reinforcement Learning requires Inference Strategies—0
Interactive OT Gym: A Reinforcement Learning-Based Interactive Optical tweezer (OT)-Driven Microrobotics Simulation Platform—0
Refining Few-Step Text-to-Multiview Diffusion via Reinforcement LearningCode0
VLMLight: Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning—0
MedDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support—0
Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning—0
Interleaved Reasoning for Large Language Models via Reinforcement Learning—0
Done Is Better than Perfect: Unlocking Efficient Reasoning by Structured Multi-Turn Decomposition—0
Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback—0
Unveiling the Compositional Ability Gap in Vision-Language Reasoning ModelCode0
Incentivizing Reasoning from Weak SupervisionCode0
TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning—0
Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network—0
What Can RL Bring to VLA Generalization? An Empirical Study—0
DISCOVER: Automated Curricula for Sparse-Reward Reinforcement LearningCode0
MT^3: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning—0
Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RLCode0
VerIPO: Cultivating Long Reasoning in Video-LLMs via Verifier-Gudied Iterative Policy OptimizationCode0
Semi-pessimistic Reinforcement Learning—0
FedORA: Resource Allocation for Federated Learning in ORAN using Radio Intelligent Controllers—0
A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement LearningCode0
Reduce Computational Cost In Deep Reinforcement Learning Via Randomized Policy Learning—0
Reinforced Latent Reasoning for LLM-based Recommendation—0
TextDiffuser-RL: Efficient and Robust Text Layout Optimization for High-Fidelity Text-to-Image Synthesis—0
The Overthinker's DIET: Cutting Token Calories with DIfficulty-AwarE Training—0
Hybrid Latent Reasoning via Reinforcement LearningCode0
GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning—0
Steering LLM Reasoning Through Bias-Only Adaptation—0
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning—0
AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware BudgetingCode0
On the Effect of Negative Gradient in Group Relative Deep Reinforcement Optimization—0
Guided by Guardrails: Control Barrier Functions as Safety Instructors for Robotic Learning—0
One Policy but Many Worlds: A Scalable Unified Policy for Versatile Humanoid Locomotion—0
Diffusion Blend: Inference-Time Multi-Preference Alignment for Diffusion ModelsCode0
WiNGPT-3.0 Technical ReportCode0
Alignment and Safety of Diffusion Models via Reinforcement Learning and Reward Modeling: A Survey—0
Diffusion Self-Weighted Guidance for Offline Reinforcement Learning—0
One RL to See Them All: Visual Triple Unified Reinforcement Learning—0
Reinforcement Speculative Decoding for Fast Ranking—0
Thinking Fast and Right: Balancing Accuracy and Reasoning Length with Adaptive RewardsCode0
Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games—0
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers—0
VL-SAFE: Vision-Language Guided Safety-Aware Reinforcement Learning with World Models for Autonomous Driving—0
Reward-Aware Proto-Representations in Reinforcement Learning—0
RAP: Runtime-Adaptive Pruning for LLM Inference—0
Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning—0
Find the Fruit: Designing a Zero-Shot Sim2Real Deep RL Planner for Occlusion Aware Plant Manipulation—0
Control of Renewable Energy Communities using AI and Real-World Data—0
AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning—0
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Benchmark Results

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