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Jeonghye Kim

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#artificial intelligence Preprint Sep 2026

Shockingly Simple Self-retrospection Improves Agentic Models Without RL

Retrospection-Only Fine-Tuning (ROFT), a minimal online procedure designed to isolate the effect of explanation-only training on subsequent behavior, is studied, showing that learning to explain can also improve learning to do and motivating further study of explanation-to-action transfer.

Jonathan Light, C. Cui, Jeonghye Kim et al. · 0 citations
#artificial intelligence Preprint Sep 2026

TISD: On-Policy Self-Distillation with Trajectory Intervention

A simple branch-regenerate-distill algorithm, Trajectory-Intervention Self-Distillation (TISD), which forces a teacher-selected branch action, returns suffix generation to the student, and distills the full trajectory under the privileged-context-conditioned teacher, support teacher-guided branching as a way to expose...

Taeckyung Lee, Rinat Amankos, Jeonghye Kim et al. · 0 citations
#artificial intelligence Preprint Sep 2026

ProgramDistill: From Interactive Web Apps to Verifiable Reference-Guided SWE Tasks

ProgramDistill is introduced, a benchmark evaluating coding agents on features discovered through interaction with fully functional reference applications, and provides a scalable benchmark with controlled difficulty for evaluating and diagnosing coding agents, and a natural basis for future curriculum-based training.

Jeonghye Kim, Minseon Kim, Young Jin Kim et al. · 0 citations
#artificial intelligence Preprint Sep 2026

FrogNano: Training a 4B Coding Agent via Online Task Synthesis

We present FrogNano, a 4B coding agent designed to tackle software engineering (SWE) tasks efficiently and effectively, even under resource-constrained environments. It is post-trained exclusively via RL on around 1,500 SWE environments with synthetic tasks. A key ingredient for improving performance is an online task...

Minseon Kim, Zheng-Yan Shi, Emiliano Penaloza et al. · 2 citations

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