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 1–25 of 15113 papers

TitleStatusHype
From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning—0
VAR-MATH: Probing True Mathematical Reasoning in Large Language Models via Symbolic Multi-Instance Benchmarks—0
QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation—0
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities—0
Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)—0
Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback—0
Fly, Fail, Fix: Iterative Game Repair with Reinforcement Learning and Large Multimodal Models—0
Kevin: Multi-Turn RL for Generating CUDA Kernels—0
Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training—0
Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across ModalitiesCode0
Personalized Exercise Recommendation with Semantically-Grounded Knowledge TracingCode0
Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light—0
Local Pairwise Distance Matching for Backpropagation-Free Reinforcement Learning—0
High-Throughput Distributed Reinforcement Learning via Adaptive Policy SynchronizationCode0
Exploring the robustness of TractOracle methods in RL-based tractographyCode0
Real-Time Bayesian Detection of Drift-Evasive GNSS Spoofing in Reinforcement Learning Based UAV Deconfliction—0
Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data ContaminationCode1
Deep Reinforcement Learning with Gradient Eligibility TracesCode1
A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement LearningCode1
The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs—0
Scaling RL to Long VideosCode0
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
High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement LearningCode2
AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMsCode2
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Benchmark Results

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