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 1521–1530 of 15113 papers

TitleStatusHype
Adaptive teachers for amortized samplersCode0
Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models—0
Scalable Reinforcement Learning-based Neural Architecture Search—0
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space—0
VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit AssignmentCode2
PreND: Enhancing Intrinsic Motivation in Reinforcement Learning through Pre-trained Network Distillation—0
Sampling from Energy-based Policies using Diffusion—0
Absolute State-wise Constrained Policy Optimization: High-Probability State-wise Constraints Satisfaction—0
Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model PretrainingCode1
Personalisation via Dynamic Policy Fusion—0
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Benchmark Results

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