Papers by Ju-Seung Byun
ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI Feedback (2024.emnlp-main)
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| Challenge: | Large Multimodal Models excel at comprehending human instructions and demonstrate remarkable results across a broad spectrum of tasks. |
| Approach: | They propose an algorithm that alters REinforcement Learning and Supervised Fine-Tuning to refine large multimodal models with specific preferences. |
| Outcome: | The proposed algorithm achieves 70% win rate compared to baseline models judged by GPT-4o. |