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 311–320 of 15113 papers

TitleStatusHype
LARES: Latent Reasoning for Sequential Recommendation—0
ARPO:End-to-End Policy Optimization for GUI Agents with Experience ReplayCode2
Reinforcement Learning for Stock Transactions—0
SophiaVL-R1: Reinforcing MLLMs Reasoning with Thinking RewardCode2
Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement LearningCode3
SATURN: SAT-based Reinforcement Learning to Unleash Language Model ReasoningCode0
Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains—0
SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine TranslationCode0
PyTupli: A Scalable Infrastructure for Collaborative Offline Reinforcement Learning ProjectsCode0
DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation—0
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Benchmark Results

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