Papers with reinforcement learning models

2 papers
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension (D19-58)

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Challenge: Existing question reformulation models are based on supervised question labels without considering feedback information from answers.
Approach: They propose a question reformulation model that integrates conversational history information with reinforcement learning.
Outcome: The proposed model is more effective in conversational machine comprehension with reinforcement learning.
VLASCD: A Visual Language Action Model for Simultaneous Chatting and Decision Making (2025.emnlp-main)

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Challenge: Recent large-scale pretrained models are built upon a multi-input single-output paradigm . tasks compete for a shared output channel, creating mutual exclusion effects .
Approach: They propose a multi-input single-output (MISO) paradigm for large pretrained models . they propose unified training framework that enables concurrent multi-task outputs .
Outcome: Experiments on autonomous driving platform show that MIMO-VLA outperforms state-of-the-art models in MIMO settings.

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