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September 23, 20250 citationsOpen Access

Optimizing Long-Term Outcomes in Tutoring with Reinforcement Learning and LLMs

Efficient RL for optimizing conversation level outcomes with an LLM-based tutor

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This research demonstrates improved long-term student engagement in math tutoring using latent state representation and reinforcement learning.

Long-term outcomes improved by optimizing tutor behavior based on latent state representations of students.
Experiments show enhancements in tutoring effectiveness, particularly in multi-turn dialogue settings.
Lightweight model design minimizes computational resources compared to previous end-to-end training methods.
Using latent states allows for better alignment with students' long-term learning goals in math.

Nam et al. (2025) studied this question.

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September 23, 20250 citationsOpen Access

Efficient RL for optimizing conversation level outcomes with an LLM-based tutor

View Full Paper

Ask AI

Bookmark

Authors

This research demonstrates improved long-term student engagement in math tutoring using latent state representation and reinforcement learning.

Long-term outcomes improved by optimizing tutor behavior based on latent state representations of students.
Experiments show enhancements in tutoring effectiveness, particularly in multi-turn dialogue settings.
Lightweight model design minimizes computational resources compared to previous end-to-end training methods.
Using latent states allows for better alignment with students' long-term learning goals in math.

Nam et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark