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 376–400 of 15113 papers

TitleStatusHype
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