Papers with Soft Actor-Critic

2 papers
Exploring Supervised and Unsupervised Rewards in Machine Translation (2021.eacl-main)

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Challenge: Autoregressive sequence-to-sequence (seq2sequ) neural architectures have become the de facto approach in Machine Translation (MT).
Approach: They propose to make models less reliant on cross-entropy loss and evaluation metrics . they propose an entropicity-regularised RL method that explores the action space .
Outcome: The proposed method exploits the action space and unsupervised reward function to balance between exploration and exploitation.
RLShield: Dynamic Jailbreak Detection for LLMs via Reinforced Adaptive Learning (2026.findings-acl)

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Challenge: Existing approaches to detect jailbreak prompts rely on static model components or fixed decision thresholds.
Approach: They propose a dynamic jailbreak detection framework that employs reinforcement learning for adaptive threshold selection.
Outcome: Experimental results show that the framework outperforms baselines in detection performance while maintaining high computational efficiency.

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