4 citations · 8 across the 13 of their papers we have counts for
4 papers · 1 filter
HarnessTTT: Learn to Optimize Harness at Test-Time for Scientific Discovery
Yingzi Ma, Yulong Cao, Nanxi Li +5
Scientific discovery with language models requires finding the best valid solution within a finite computational budget. We study how feedback from this search can improve the proc…
Off-Manifold Collapse in Guided Protein Language Models
Shuibai Zhang, Xinchi Liu, Fred Zhangzhi Peng +4
Protein language models are widely used priors for protein sequence design, and a growing body of work controls them at inference time as an alternative to fine-tuning. Such guidan…
One Student, Many Teachers: Multi-Task On-Policy Distillation via Soft-Prompt Privileged Context
Yingzi Ma, Zichen Zhu, Ming Jiang +1
On-policy self-distillation (OPSD) teaches large language models new skills through a teacher that shares the student's backbone and supervises its own rollouts. Existing teachers…
When Are Teacher Tokens Reliable? Position-Weighted On-Policy Self-Distillation for Reasoning
Xiaogeng Liu, Xinyan Wang, Yingzi Ma +2
On-policy self-distillation (OPSD) trains a student on its own rollouts using a privileged teacher, but its standard objective weights all generated tokens equally, implicitly trea…