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 2701–2725 of 15113 papers

TitleStatusHype
Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning—0
SwitchMT: An Adaptive Context Switching Methodology for Scalable Multi-Task Learning in Intelligent Autonomous Agents—0
Improving Generalization in Intent Detection: GRPO with Reward-Based Curriculum Sampling—0
Evolutionary Policy Optimization—0
TraCeS: Trajectory Based Credit Assignment From Sparse Safety Feedback—0
Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration—0
LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard—0
RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs—0
Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent RegimeCode0
pix2pockets: Shot Suggestions in 8-Ball Pool from a Single Image in the Wild—0
ToolRL: Reward is All Tool Learning NeedsCode0
Evolutionary Reinforcement Learning for Interpretable Decision-Making in Supply Chain Management—0
d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning—0
VIPO: Value Function Inconsistency Penalized Offline Reinforcement Learning—0
ReTool: Reinforcement Learning for Strategic Tool Use in 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
Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs—0
Revealing Covert Attention by Analyzing Human and Reinforcement Learning Agent Gameplay—0
Hallucination-Aware Generative Pretrained Transformer for Cooperative Aerial Mobility Control—0
Next-Future: Sample-Efficient Policy Learning for Robotic-Arm Tasks—0
ReZero: Enhancing LLM search ability by trying one-more-time—0
Adaptive Insurance Reserving with CVaR-Constrained Reinforcement Learning under Macroeconomic Regimes—0
CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agent—0
Efficient Implementation of Reinforcement Learning over Homomorphic Encryption—0
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Benchmark Results

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