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 126–150 of 15113 papers

TitleStatusHype
TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy OptimizationCode0
Policy-Based Trajectory Clustering in Offline Reinforcement Learning—0
Offline RL with Smooth OOD Generalization in Convex Hull and its NeighborhoodCode0
DeepForm: Reasoning Large Language Model for Communication System Formulation—0
Exploration by Random Reward Perturbation—0
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
SPEED-RL: Faster Training of Reasoning Models via Online Curriculum LearningCode1
Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement LearningCode2
RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic SamplingCode1
Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)—0
MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning—0
How to Provably Improve Return Conditioned Supervised Learning?—0
Reinforcement Learning Teachers of Test Time Scaling—0
Intention-Conditioned Flow Occupancy ModelsCode1
DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO—0
Play to Generalize: Learning to Reason Through Game PlayCode2
Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest QuestionsCode2
Through the Valley: Path to Effective Long CoT Training for Small Language Models—0
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information—0
Reinforcement Pre-Training—0
AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking—0
LUCIFER: Language Understanding and Context-Infused Framework for Exploration and Behavior Refinement—0
WeThink: Toward General-purpose Vision-Language Reasoning via Reinforcement LearningCode1
Thinking vs. Doing: Agents that Reason by Scaling Test-Time InteractionCode2
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
Show:102550
← PrevPage 6 of 605Next →

Benchmark Results

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