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 11–20 of 15113 papers

TitleStatusHype
SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the WildCode7
An Empirical Study on Reinforcement Learning for Reasoning-Search Interleaved LLM AgentsCode7
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning AttentionCode7
Kimi k1.5: Scaling Reinforcement Learning with LLMsCode7
Flow-GRPO: Training Flow Matching Models via Online RLCode7
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement LearningCode7
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement LearningCode7
The Dormant Neuron Phenomenon in Deep Reinforcement LearningCode6
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement LearningCode6
Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real TransferCode5
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Benchmark Results

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