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 301–325 of 15113 papers

TitleStatusHype
Reinforcement Learning for Ballbot Navigation in Uneven TerrainCode1
Co-Reinforcement Learning for Unified Multimodal Understanding and GenerationCode1
The Cell Must Go On: Agar.io for Continual Reinforcement LearningCode1
Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQLCode3
RAP: Runtime-Adaptive Pruning for LLM Inference—0
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers—0
Control of Renewable Energy Communities using AI and Real-World Data—0
R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPOCode3
PyTupli: A Scalable Infrastructure for Collaborative Offline Reinforcement Learning ProjectsCode0
Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games—0
Reward-Aware Proto-Representations in Reinforcement Learning—0
SophiaVL-R1: Reinforcing MLLMs Reasoning with Thinking RewardCode2
SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine TranslationCode0
AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning—0
Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning—0
LARES: Latent Reasoning for Sequential Recommendation—0
WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement LearningCode2
Delving into RL for Image Generation with CoT: A Study on DPO vs. GRPOCode4
Distilling the Implicit Multi-Branch Structure in LLMs' Reasoning via Reinforcement Learning—0
SATURN: SAT-based Reinforcement Learning to Unleash Language Model ReasoningCode0
Strategically Linked Decisions in Long-Term Planning and Reinforcement Learning—0
Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement LearningCode3
ARPO:End-to-End Policy Optimization for GUI Agents with Experience ReplayCode2
Dynamic Sampling that Adapts: Iterative DPO for Self-Aware Mathematical Reasoning—0
Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains—0
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Benchmark Results

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