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

Showing 101125 of 15113 papers

TitleStatusHype
Adversarial Cheap TalkCode3
Demystifying Long Chain-of-Thought Reasoning in LLMsCode3
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning AlgorithmsCode3
Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRsCode3
A Clean Slate for Offline Reinforcement LearningCode3
Learning to Reason under Off-Policy GuidanceCode3
Learning Bipedal Walking for Humanoids with Current FeedbackCode3
DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing ReasoningCode3
Learning Bipedal Walking On Planned Footsteps For Humanoid RobotsCode3
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement LearningCode3
imitation: Clean Imitation Learning ImplementationsCode3
ACEGEN: Reinforcement learning of generative chemical agents for drug discoveryCode3
Is Value Learning Really the Main Bottleneck in Offline RL?Code3
Reinforcement Learning Outperforms Supervised Fine-Tuning: A Case Study on Audio Question AnsweringCode3
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid AlgorithmsCode3
Generating Synergistic Formulaic Alpha Collections via Reinforcement LearningCode3
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise RewardCode3
Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement LearningCode3
Flow Q-LearningCode3
General-Reasoner: Advancing LLM Reasoning Across All DomainsCode3
Fine-Tuning Language Models from Human PreferencesCode3
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative FinanceCode3
Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQLCode3
Test-Time Training Scaling Laws for Chemical Exploration in Drug DesignCode3
ExTrans: Multilingual Deep Reasoning Translation via Exemplar-Enhanced Reinforcement LearningCode3
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Benchmark Results

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