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Reinforcement Learning (RL)
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Fully Offline Reinforcement Learning
The Price of Failure Risk: Clipped Barrier Policy Gradients with Convex Regularization
From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning
Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)
VAR-MATH: Probing True Mathematical Reasoning in Large Language Models via Symbolic Multi-Instance Benchmarks
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities
Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback
QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation
Kevin: Multi-Turn RL for Generating CUDA Kernels
Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training
Fly, Fail, Fix: Iterative Game Repair with Reinforcement Learning and Large Multimodal Models
High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization
Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing
Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across Modalities
Real-Time Bayesian Detection of Drift-Evasive GNSS Spoofing in Reinforcement Learning Based UAV Deconfliction
Local Pairwise Distance Matching for Backpropagation-Free Reinforcement Learning
Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light
Exploring the robustness of TractOracle methods in RL-based tractography
Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination
Deep Reinforcement Learning with Gradient Eligibility Traces
A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement Learning
The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs
Scaling RL to Long Videos
Video-RTS: Rethinking Reinforcement Learning and Test-Time Scaling for Efficient and Enhanced Video Reasoning
Squeeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model
Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study
AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs
Robust Bandwidth Estimation for Real-Time Communication with Offline Reinforcement Learning
GTA1: GUI Test-time Scaling Agent
High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning
CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation
FEVO: Financial Knowledge Expansion and Reasoning Evolution for Large Language Models
Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation
Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for Visual Reasoning
2048: Reinforcement Learning in a Delayed Reward Environment
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