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 501–510 of 15113 papers

TitleStatusHype
RM-R1: Reward Modeling as ReasoningCode2
EMORL: Ensemble Multi-Objective Reinforcement Learning for Efficient and Flexible LLM Fine-TuningCode0
Automated Hybrid Reward Scheduling via Large Language Models for Robotic Skill Learning—0
Exploring the Potential of Offline RL for Reasoning in LLMs: A Preliminary Study—0
Prompt-responsive Object Retrieval with Memory-augmented Student-Teacher Learning—0
A Generalised and Adaptable Reinforcement Learning Stopping MethodCode0
Analytic Energy-Guided Policy Optimization for Offline Reinforcement Learning—0
World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks—0
Stabilizing Temporal Difference Learning via Implicit Stochastic Recursion—0
Directly Forecasting Belief for Reinforcement Learning with DelaysCode0
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Benchmark Results

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