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 2491–2500 of 15113 papers

TitleStatusHype
Thinking Fast and Right: Balancing Accuracy and Reasoning Length with Adaptive RewardsCode0
Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games—0
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers—0
VL-SAFE: Vision-Language Guided Safety-Aware Reinforcement Learning with World Models for Autonomous Driving—0
Reward-Aware Proto-Representations in Reinforcement Learning—0
RAP: Runtime-Adaptive Pruning for LLM Inference—0
Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning—0
Find the Fruit: Designing a Zero-Shot Sim2Real Deep RL Planner for Occlusion Aware Plant Manipulation—0
Control of Renewable Energy Communities using AI and Real-World Data—0
AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning—0
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Benchmark Results

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