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Tail-Likelihood Reinforcement Learning
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reinforcement learninghigh-reward optimizationpolicy evaluation
2609.02987
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1h ago80%
Abstract
The paper introduces Tail-Likelihood Reinforcement Learning (TailRL), which optimizes the likelihood of exceeding high-reward thresholds in reinforcement learning.
Reality Card
Core Claim
TailRL effectively maximizes the log-probability of exceeding a randomly chosen reward threshold, improving model performance by leveraging rare high-reward samples.
Method / Result
TailRL yields models that benefit more from additional samples at inference time across various tasks.
Limitations
The paper does not specify potential limitations or reproducibility concerns.
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