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 1881–1890 of 15113 papers

TitleStatusHype
RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-FoldCode1
Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing—0
MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency TradingCode2
Beyond Optimism: Exploration With Partially Observable RewardsCode0
Revealing the learning process in reinforcement learning agents through attention-oriented metrics—0
Optimizing Novelty of Top-k Recommendations using Large Language Models and Reinforcement Learning—0
Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue—0
Learned Graph Rewriting with Equality Saturation: A New Paradigm in Relational Query Rewrite and Beyond—0
Oralytics Reinforcement Learning AlgorithmCode0
Optimizing Wireless Discontinuous Reception via MAC Signaling Learning—0
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Benchmark Results

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