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 551–575 of 15113 papers

TitleStatusHype
Policy-Based Radiative Transfer: Solving the 2-Level Atom Non-LTE Problem using Soft Actor-Critic Reinforcement Learning—0
SARI: Structured Audio Reasoning via Curriculum-Guided Reinforcement Learning—0
StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation—0
Insights from Verification: Training a Verilog Generation LLM with Reinforcement Learning with Testbench Feedback—0
LAPP: Large Language Model Feedback for Preference-Driven Reinforcement Learning—0
Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL—0
OTC: Optimal Tool Calls via Reinforcement Learning—0
FlowReasoner: Reinforcing Query-Level Meta-AgentsCode2
Learning to Reason under Off-Policy GuidanceCode3
Dynamic Contrastive Skill Learning with State-Transition Based Skill Clustering and Dynamic Length Adjustment—0
Stop Summation: Min-Form Credit Assignment Is All Process Reward Model Needs for ReasoningCode2
Relation-R1: Cognitive Chain-of-Thought Guided Reinforcement Learning for Unified Relational Comprehension—0
Generative Auto-Bidding with Value-Guided ExplorationsCode2
Mixed-Precision Conjugate Gradient Solvers with RL-Driven Precision Tuning—0
Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment—0
Unlearning Works Better Than You Think: Local Reinforcement-Based Selection of Auxiliary Objectives—0
Improving RL Exploration for LLM Reasoning through Retrospective Replay—0
Compile Scene Graphs with Reinforcement LearningCode1
Prejudge-Before-Think: Enhancing Large Language Models at Test-Time by Process Prejudge ReasoningCode0
Improving Generalization in Intent Detection: GRPO with Reward-Based Curriculum Sampling—0
Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning—0
SwitchMT: An Adaptive Context Switching Methodology for Scalable Multi-Task Learning in Intelligent Autonomous Agents—0
NoisyRollout: Reinforcing Visual Reasoning with Data AugmentationCode2
Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement LearningCode2
LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard—0
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Benchmark Results

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