This aligns the density of rewards and action spaces more closely with the information density of the input. Experiments demonstrate that our method can be ...
Oct 23, 2024 · This aligns the density of rewards and action spaces more closely with the information density of the input. Experiments demonstrate that our ...
Dec 4, 2024 · This demonstrates the need to align the density gap of the information within the sequence with the reward signal to reduce the total reward ...
Dec 5, 2024 · This aligns the density of rewards and action spaces more closely with the information density of the input. Experiments demonstrate that our ...
This demonstrates the need to align the density gap of the information within the sequence with the reward signal to reduce the total reward error, thereby ...
Dec 4, 2024 · This paper explores the gap between action and reward spaces in reinforcement learning (RL) for AI alignment. The authors propose a method ...
Co-authors ; Adaptive Dense Reward: Understanding the Gap Between Action and Reward Space in Alignment. Y Li, S Xiong, G Chen, X Li, Y Luo, X Zhang, Y Huang, X ...
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Adaptive Dense Reward: Understanding the Gap Between Action and Reward Space in Alignment ... Our method improves the success rate on adversarial samples by 10\% ...
Adaptive Dense Reward: Understanding the Gap Between Action and Reward Space in Alignment · no code implementations • 23 Oct 2024 • Yanshi Li, Shaopan Xiong ...
... Adaptive Message-wise RLHF.. Adaptive Dense Reward: Understanding the Gap Between Action and Reward Space in Alignment. Preprint. Full-text available. Oct 2024.