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 371–380 of 15113 papers

TitleStatusHype
AAPO: Enhance the Reasoning Capabilities of LLMs with Advantage Momentum—0
Think-J: Learning to Think for Generative LLM-as-a-JudgeCode0
Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models—0
s3: You Don't Need That Much Data to Train a Search Agent via RLCode4
TinyV: Reducing False Negatives in Verification Improves RL for LLM ReasoningCode1
UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning—0
Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks—0
NavBench: A Unified Robotics Benchmark for Reinforcement Learning-Based Autonomous Navigation—0
APEX: Empowering LLMs with Physics-Based Task Planning for Real-time InsightCode0
Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning—0
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Benchmark Results

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