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 576–600 of 15113 papers

TitleStatusHype
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