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

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Showing 131–140 of 15113 papers

TitleStatusHype
Exploration by Random Reward Perturbation—0
Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement LearningCode2
RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic SamplingCode1
Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)—0
MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning—0
How to Provably Improve Return Conditioned Supervised Learning?—0
Offline RL with Smooth OOD Generalization in Convex Hull and its NeighborhoodCode0
Reinforcement Learning Teachers of Test Time Scaling—0
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking—0
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Benchmark Results

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