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cs.AI2026
PACED: Distillation and On-Policy Self-Distillation at the Frontier of Student Competence
Yuanda Xu, Hejian Sang, Zhengze Zhou +2
Standard LLM distillation treats all training problems equally -- wasting compute on problems the student has already mastered or cannot yet solve. We empirically show that this in…
cs.AI2026
Scaling In-Context Online Learning Capability of LLMs via Cross-Episode Meta-RL
Xiaofeng Lin, Sirou Zhu, Yilei Chen +6
Large language models (LLMs) achieve strong performance when all task-relevant information is available upfront, as in static prediction and instruction-following problems. However…
cs.AI2025
Planner-R1: Reward Shaping Enables Efficient Agentic RL with Smaller LLMs
Siyu Zhu, Yanbin Jiang, Hejian Sang +6
We investigated Agentic RL with large language models on the \textsc{TravelPlanner} benchmark. Our approach, \textsc{Planner-R1}, achieved a \textbf{56.9\%} final-pass rate with on…