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 551–600 of 15113 papers

TitleStatusHype
Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model—0
SARI: Structured Audio Reasoning via Curriculum-Guided Reinforcement Learning—0
StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation—0
Insights from Verification: Training a Verilog Generation LLM with Reinforcement Learning with Testbench Feedback—0
LAPP: Large Language Model Feedback for Preference-Driven Reinforcement Learning—0
Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL—0
FlowReasoner: Reinforcing Query-Level Meta-AgentsCode2
Stop Summation: Min-Form Credit Assignment Is All Process Reward Model Needs for ReasoningCode2
Learning to Reason under Off-Policy GuidanceCode3
OTC: Optimal Tool Calls via Reinforcement Learning—0
Dynamic Contrastive Skill Learning with State-Transition Based Skill Clustering and Dynamic Length Adjustment—0
Relation-R1: Cognitive Chain-of-Thought Guided Reinforcement Learning for Unified Relational Comprehension—0
Generative Auto-Bidding with Value-Guided ExplorationsCode2
Mixed-Precision Conjugate Gradient Solvers with RL-Driven Precision Tuning—0
Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment—0
Improving RL Exploration for LLM Reasoning through Retrospective Replay—0
Unlearning Works Better Than You Think: Local Reinforcement-Based Selection of Auxiliary Objectives—0
Improving Generalization in Intent Detection: GRPO with Reward-Based Curriculum Sampling—0
Prejudge-Before-Think: Enhancing Large Language Models at Test-Time by Process Prejudge ReasoningCode0
Compile Scene Graphs with Reinforcement LearningCode1
SwitchMT: An Adaptive Context Switching Methodology for Scalable Multi-Task Learning in Intelligent Autonomous Agents—0
Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning—0
Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement LearningCode2
Evolutionary Policy Optimization—0
TraCeS: Trajectory Based Credit Assignment From Sparse Safety Feedback—0
LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard—0
NoisyRollout: Reinforcing Visual Reasoning with Data AugmentationCode2
Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration—0
RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs—0
SkyReels-V2: Infinite-length Film Generative ModelCode9
ToolRL: Reward is All Tool Learning NeedsCode0
VIPO: Value Function Inconsistency Penalized Offline Reinforcement Learning—0
pix2pockets: Shot Suggestions in 8-Ball Pool from a Single Image in the Wild—0
d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning—0
Evolutionary Reinforcement Learning for Interpretable Decision-Making in Supply Chain Management—0
Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent RegimeCode0
ReTool: Reinforcement Learning for Strategic Tool Use in LLMs—0
Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs—0
Data driven approach towards more efficient Newton-Raphson power flow calculation for distribution gridsCode0
Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling—0
Revealing Covert Attention by Analyzing Human and Reinforcement Learning Agent Gameplay—0
A Clean Slate for Offline Reinforcement LearningCode3
Hallucination-Aware Generative Pretrained Transformer for Cooperative Aerial Mobility Control—0
Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation ModelsCode4
Next-Future: Sample-Efficient Policy Learning for Robotic-Arm Tasks—0
DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing ReasoningCode3
A Minimalist Approach to LLM Reasoning: from Rejection Sampling to ReinforceCode3
ReZero: Enhancing LLM search ability by trying one-more-time—0
MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement LearningCode2
CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agent—0
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Benchmark Results

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