Training open-weight LLMs on conversational serializations of authentic student programming submissions produces artificial learners that better replicate real debugging behavior than code-only baselines or prompted large models.
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Diffusion-APO synchronizes training noise with inference trajectories in video diffusion models to improve preference alignment and visual quality.
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Teaching Language Models How to Code Like Learners: Conversational Serialization for Student Simulation
Training open-weight LLMs on conversational serializations of authentic student programming submissions produces artificial learners that better replicate real debugging behavior than code-only baselines or prompted large models.
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Diffusion-APO: Trajectory-Aware Direct Preference Alignment for Video Diffusion Transformers
Diffusion-APO synchronizes training noise with inference trajectories in video diffusion models to improve preference alignment and visual quality.