5 papers
Where Do Distilled Students Store Thinking?
Taeyeong Kim, Jaemin Jung, Ahhyun Kim +1
Small students degrade on large reasoning models' (LRMs') long solutions, so recent work shortens them by interpolating an LRM with a short-answer model at ratio . We ask what s…
SpecialEduBench: Benchmarking Vision-Language Models on Knowledge, Skill, and Attitude in Language Intervention for Autistic Children
Jihoi Na, Taeyeong Kim, Sungjune Kong +11
Language is the target of most early intervention for autistic children. Because the goal and the method change from child to child, the work falls to a teacher who takes one child…
Not the Dimension, the Norm: What Matters in Gradient-Free Weight Perturbation of Language Models
Taeyeong Kim, Ahhyun Kim, TaeHyeon Kim +1
Adapting a language model to a task no longer requires training all of its weights, and a line of parameter-efficient methods has driven the trainable count from billions down to a…
Pedagogical Alignment for Vision-Language-Action Models: A Comprehensive Framework for Data, Architecture, and Evaluation in Education
Unggi Lee, Jahyun Jeong, Sunyoung Shin +12
Science demonstrations are important for effective STEM education, yet teachers face challenges in conducting them safely and consistently across multiple occasions, where robotics…
AdaSTaR: Adaptive Data Sampling for Training Self-Taught Reasoners
Woosung Koh, Wonbeen Oh, Jaein Jang +7
Self-Taught Reasoners (STaR), synonymously known as Rejection sampling Fine-Tuning (RFT), is an integral part of the training pipeline of self-improving reasoning Language Models (…