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 2521–2530 of 15113 papers

TitleStatusHype
VerifyBench: Benchmarking Reference-based Reward Systems for Large Language Models—0
A Temporal Difference Method for Stochastic Continuous DynamicsCode0
Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning—0
Learning-based Autonomous Oversteer Control and Collision Avoidance—0
When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning—0
Thought-Augmented Policy Optimization: Bridging External Guidance and Internal Capabilities—0
STAR-R1: Spacial TrAnsformation Reasoning by Reinforcing Multimodal LLMsCode0
ViaRL: Adaptive Temporal Grounding via Visual Iterated Amplification Reinforcement Learning—0
GRIT: Teaching MLLMs to Think with Images—0
LLM-Explorer: A Plug-in Reinforcement Learning Policy Exploration Enhancement Driven by Large Language Models—0
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Benchmark Results

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