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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2401–2410 of 15113 papers

TitleStatusHype
Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem—0
Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback—0
KDRL: Post-Training Reasoning LLMs via Unified Knowledge Distillation and Reinforcement Learning—0
Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains—0
Trajectory First: A Curriculum for Discovering Diverse Policies—0
Data-assimilated model-informed reinforcement learning—0
SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning—0
DriveMind: A Dual-VLM based Reinforcement Learning Framework for Autonomous Driving—0
A Reinforcement Learning Approach for RIS-aided Fair Communications—0
ARIA: Training Language Agents with Intention-Driven Reward Aggregation—0
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Benchmark Results

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