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 351–400 of 15113 papers

TitleStatusHype
Learn to Reason Efficiently with Adaptive Length-based Reward ShapingCode2
A Temporal Difference Method for Stochastic Continuous DynamicsCode0
Pass@K Policy Optimization: Solving Harder Reinforcement Learning Problems—0
MMaDA: Multimodal Large Diffusion Language ModelsCode0
Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectives—0
STAR-R1: Spacial TrAnsformation Reasoning by Reinforcing Multimodal LLMsCode0
Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One—0
VARD: Efficient and Dense Fine-Tuning for Diffusion Models with Value-based RL—0
LLM-Explorer: A Plug-in Reinforcement Learning Policy Exploration Enhancement Driven by Large Language Models—0
Learning-based Autonomous Oversteer Control and Collision Avoidance—0
ViaRL: Adaptive Temporal Grounding via Visual Iterated Amplification Reinforcement Learning—0
HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Driving—0
Guided Policy Optimization under Partial ObservabilityCode0
Bellman operator convergence enhancements in reinforcement learning algorithms—0
RL of Thoughts: Navigating LLM Reasoning with Inference-time Reinforcement Learning—0
Self-Evolving Curriculum for LLM Reasoning—0
KIPPO: Koopman-Inspired Proximal Policy Optimization—0
Normalized Cut with Reinforcement Learning in Constrained Action Space—0
Think-J: Learning to Think for Generative LLM-as-a-JudgeCode0
Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning—0
AAPO: Enhance the Reasoning Capabilities of LLMs with Advantage Momentum—0
s3: You Don't Need That Much Data to Train a Search Agent via RLCode4
UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning—0
Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models—0
TinyV: Reducing False Negatives in Verification Improves RL for LLM ReasoningCode1
General-Reasoner: Advancing LLM Reasoning Across All DomainsCode3
APEX: Empowering LLMs with Physics-Based Task Planning for Real-time InsightCode0
NavBench: A Unified Robotics Benchmark for Reinforcement Learning-Based Autonomous Navigation—0
Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks—0
Benchmarking MOEAs for solving continuous multi-objective RL problemsCode0
Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning—0
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis—0
Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision TraceabilityCode1
ToTRL: Unlock LLM Tree-of-Thoughts Reasoning Potential through Puzzles Solving—0
On-Policy Optimization with Group Equivalent Preference for Multi-Programming Language Understanding—0
Counterfactual Explanations for Continuous Action Reinforcement LearningCode0
Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMsCode1
G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement LearningCode2
Your Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimization—0
DGRO: Enhancing LLM Reasoning via Exploration-Exploitation Control and Reward Variance Management—0
J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization—0
Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs—0
Optimizing Anytime Reasoning via Budget Relative Policy OptimizationCode2
Temporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning—0
Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach—0
Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning—0
ExTrans: Multilingual Deep Reasoning Translation via Exemplar-Enhanced Reinforcement LearningCode3
Synthetic Data RL: Task Definition Is All You NeedCode2
Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents—0
Resolving Latency and Inventory Risk in Market Making with Reinforcement Learning—0
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Benchmark Results

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