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cs.AI2026
AutoSFT: An Agentic System for Automated Data Research in Supervised Fine-Tuning
Bingxiang He, Pengqi Sun, Shihao Peng +18
Language model agents increasingly automate post-training, but when given the full training stack they converge on supervised fine-tuning and spend most effort revising training da…
cs.AI2026
Rethinking On-Policy Distillation of Large Language Models II: One Training Example
Zixuan Fu, Bingxiang He, Yuxin Zuo +10
On-policy distillation (OPD) combines student-generated rollouts with dense token-level supervision from a teacher. Existing work has mainly studied its algorithmic behavior, leavi…
cs.AI2025
CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents
Jiayu Liu, Cheng Qian, Zhaochen Su +4
Current evaluations of Large Language Model (LLM) agents primarily emphasize task completion, often overlooking resource efficiency and adaptability. This neglects a crucial capabi…