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arXiv · 2609.33838

ChemOPD: Multi-Teacher On-Policy Distillation for Multi-Task Chemical Reasoning

Yaoyao Xu·Xinjian Zhao·Xiaozhuang Song·Xuemin Chen·Tianshu Yu
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Abstract

Large language models are increasingly expected to support diverse chemical reasoning capabilities within a unified model. One approach is to develop specialized capabilities separately and consolidate them through multi-teacher on-policy distillation, but this raises two questions: how should specialization be organized, and how should specialist guidance be integrated? We introduce ChemOPD, which addresses both. We estimate task affinities from supervised fine-tuning gradients and solve a constrained mixed-integer program(MIP) to construct partially overlapping specialist groups. During distillation, we retain a generalist teacher trained on all tasks so that specialist guidance supplements rather than replaces its supervision. Our anchor-residual objective gradually increases the routed specialist's contribution on student-generated responses. On ChemCoTBench, affinity-guided specialization produces task-dependent gains over the generalist teacher and improves several capabilities beyond semantic task grouping. Yet stronger teacher-side performance does not automatically yield stronger students: with the same specialists and routes, anchor-residual OPD improves most reported metrics over specialist-only distillation and realizes a larger share of the available teacher gains. These results highlight specialization and capability integration as connected but distinct design problems in chemical reasoning.

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Yaoyao Xu, Xinjian Zhao, Xiaozhuang Song, Xuemin Chen, Tianshu Yu. 2026-09-27. ChemOPD: Multi-Teacher On-Policy Distillation for Multi-Task Chemical Reasoning. https://arxiv.org/abs/2609.33838

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