ChemOPD: Multi-Teacher On-Policy Distillation for Multi-Task Chemical Reasoning
cs.LG, cs.CL
Submitted: 2026-09-27
Updated: 2026-09-27
Terminology
Sources
- Counteraction-Aware Multi-Teacher On-Policy Distillation for General Capability Recovery with Domain Preservation
- Adapting Auxiliary Losses Using Gradient Similarity
- Distilling the Knowledge in a Neural Network
- Learning Task Grouping and Overlap in Multi-task Learning
- Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe
- Enhancing Chemical Reaction and Retrosynthesis Prediction with Large Language Model and Dual-task Learning
- MOPD: Multi-Teacher On-Policy Distillation for Capability Integration in LLM Post-Training
- A Survey of On-Policy Distillation for Large Language Models
- Towards Principled Task Grouping for Multi-Task Learning
- MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate
- A Survey on Knowledge Distillation of Large Language Models
- TIP: Token Importance in On-Policy Distillation
- Qwen3 Technical Report
- LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset
- ChemLLM: A Chemical Large Language Model
- MolEmb: Multimodal Large Language Models Can Be Strong Molecular Embedding Models
- ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge
- DanceOPD: On-Policy Generative Field Distillation
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