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 2501–2525 of 15113 papers

TitleStatusHype
Find the Fruit: Designing a Zero-Shot Sim2Real Deep RL Planner for Occlusion Aware Plant Manipulation—0
SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine TranslationCode0
Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning—0
Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only—0
Offline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies—0
DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation—0
SATURN: SAT-based Reinforcement Learning to Unleash Language Model ReasoningCode0
Dynamic Sampling that Adapts: Iterative DPO for Self-Aware Mathematical Reasoning—0
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)—0
Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games—0
Reward-Aware Proto-Representations in Reinforcement Learning—0
Strategically Linked Decisions in Long-Term Planning and Reinforcement Learning—0
PyTupli: A Scalable Infrastructure for Collaborative Offline Reinforcement Learning ProjectsCode0
LARES: Latent Reasoning for Sequential Recommendation—0
GRIT: Teaching MLLMs to Think with Images—0
STAR-R1: Spacial TrAnsformation Reasoning by Reinforcing Multimodal LLMsCode0
Guided Policy Optimization under Partial ObservabilityCode0
Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectives—0
VARD: Efficient and Dense Fine-Tuning for Diffusion Models with Value-based RL—0
Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning—0
HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Driving—0
A Temporal Difference Method for Stochastic Continuous DynamicsCode0
Reward Is Enough: LLMs Are In-Context Reinforcement Learners—0
Pass@K Policy Optimization: Solving Harder Reinforcement Learning Problems—0
VerifyBench: Benchmarking Reference-based Reward Systems for Large Language Models—0
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Benchmark Results

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