No Prompt Left Behind: Exploiting Zero-Variance Prompts in LLM Reinforcement Learning via Entropy-Guided Advantage Shaping
Thanh-Long V. Le · Myeongho Jeon · Kim Vu · Viet Lai · Eunho Yang
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Thanh-Long V. Le · Myeongho Jeon · Kim Vu · Viet Lai · Eunho Yang
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Recent proprietary video generation models have demonstrated remarkable proficiency in synthesizing highly realistic videos from textual instructions. Most open-source text-to-video models, however, still struggle to accurately simulate real-world physics and dynamic entity interactions. Existing...
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The inability to filter out in advance all potentially problematic data from the pre-training of large language models has given rise to the need for methods for unlearning specific pieces of knowledge after training. Existing techniques overlook the need for continuous and immediate action, caus...
Jina Kim · Myeongho Jeon · Soohyun Cho · Chae-Gyun Lim · Jongmin Lim · Haewon Min · Eunho Yang
The growing demand for scalable mental health support has increased interest in AI-based counseling systems grounded in Motivational Interviewing (MI). However, existing MI datasets do not explicitly model the structured progression of MI phases, which is essential for effective and goal-oriented...