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cs.AI2026
Learning after Execution with Action-Specific Credit
Yufeng Tian, Jiahao Tang, Minghao Wu
Execution is often used as terminal feedback: it scores a generated program, but the resulting state is not used for another learning action. Learning after execution instead uses…
cs.AI2024
Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue
Huifang Du, Shuqin Li, Minghao Wu +3
Reinforcement learning (RL) is a powerful approach to enhance task-oriented dialogue (TOD) systems. However, existing RL methods tend to mainly focus on generation tasks, such as d…