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 176–200 of 15113 papers

TitleStatusHype
Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback—0
Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem—0
Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games—0
Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains—0
SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning—0
Incentivizing Reasoning for Advanced Instruction-Following of Large Language ModelsCode1
KDRL: Post-Training Reasoning LLMs via Unified Knowledge Distillation and Reinforcement Learning—0
Trajectory First: A Curriculum for Discovering Diverse Policies—0
Data-assimilated model-informed reinforcement learning—0
Reasoning-Table: Exploring Reinforcement Learning for Table ReasoningCode2
A Reinforcement Learning Approach for RIS-aided Fair Communications—0
DriveMind: A Dual-VLM based Reinforcement Learning Framework for Autonomous Driving—0
ARIA: Training Language Agents with Intention-Driven Reward Aggregation—0
MMedAgent-RL: Optimizing Multi-Agent Collaboration for Multimodal Medical Reasoning—0
Reinforcement Learning for Hanabi—0
Balancing Profit and Fairness in Risk-Based Pricing Markets—0
MOFGPT: Generative Design of Metal-Organic Frameworks using Language ModelsCode0
Reason-SVG: Hybrid Reward RL for Aha-Moments in Vector Graphics Generation—0
Pangu DeepDiver: Adaptive Search Intensity Scaling via Open-Web Reinforcement Learning—0
How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning—0
Mixed-R1: Unified Reward Perspective For Reasoning Capability in Multimodal Large Language ModelsCode0
AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language ReasoningCode7
ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RLCode2
ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language ModelsCode5
Towards Effective Code-Integrated ReasoningCode1
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Benchmark Results

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