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 651–660 of 15113 papers

TitleStatusHype
Integrating Human Knowledge Through Action Masking in Reinforcement Learning for Operations Research—0
Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation SchemeCode2
ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement LearningCode1
De Novo Molecular Design Enabled by Direct Preference Optimization and Curriculum Learning—0
Probabilistic Curriculum Learning for Goal-Based Reinforcement Learning—0
Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?Code1
GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical ReasoningCode1
How Difficulty-Aware Staged Reinforcement Learning Enhances LLMs' Reasoning Capabilities: A Preliminary Experimental Study—0
MPCritic: A plug-and-play MPC architecture for reinforcement learningCode1
Grounding Multimodal LLMs to Embodied Agents that Ask for Help with Reinforcement Learning—0
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Benchmark Results

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