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

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Showing 2371–2380 of 15113 papers

TitleStatusHype
DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO—0
Through the Valley: Path to Effective Long CoT Training for Small Language Models—0
AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking—0
Reinforcement Pre-Training—0
Bingo: Boosting Efficient Reasoning of LLMs via Dynamic and Significance-based Reinforcement Learning—0
LUCIFER: Language Understanding and Context-Infused Framework for Exploration and Behavior Refinement—0
On the Generalization of Data-Assisted Control in port-Hamiltonian Systems (DAC-pH)—0
Learning to Clarify by Reinforcement Learning Through Reward-Weighted Fine-Tuning—0
Safety-Aware Reinforcement Learning for Control via Risk-Sensitive Action-Value Iteration and Quantile Regression—0
Reliable Critics: Monotonic Improvement and Convergence Guarantees for Reinforcement Learning—0
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Benchmark Results

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