3 papers
cs.CL2026
RegMix-D: Dynamic Data Mixing via Proxy Training Trajectories
Kaiyan Zhao, Zhongtao Miao, Akiko Aizawa +1
Data mixture selection is critical for Large Language Model pretraining. Existing methods such as RegMix select a single static mixture by fitting a regression model on small-scale…
cs.AI2026
Tailoring the Curriculum: Student-Centered Reasoning Distillation via Dynamic Data-Model Compatibility
Jiahao Huang, Fei Cheng, Junfeng Jiang +1
Reasoning distillation transfers complex reasoning abilities from large language models (LLMs) to smaller ones, yet its success depends on how well the training data align with the…
cs.AI2026
BenchTrace: A Benchmark for Testing Reflection Ability and Controlled Evolution in LLM Agents
Jiahao Huang, Fei Cheng, Junfeng Jiang +2
Self-evolving agents improve over time by reflecting on past failures, but existing evaluation is limited in two ways: it measures only task scores, leaving reflection quality unkn…