Latent-MOPD: Latent Multi-Teacher On-Policy Distillation

arXiv:2610.02381 · cs.LG · Submitted 2026-10-01 · Read on arXiv

cs.LG

Submitted: 2026-10-01

Updated: 2026-10-01

Code: https://github.com/fangzy96/Latent-MOPD

Terminology

• Distillation — 1× in this paper · explained in 2 episodes
• On-Policy Distillation — 1× in this paper · explained in 3 episodes

Sources

• OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
• Program Synthesis with Large Language Models
• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• Poly-OPD: Heterogeneous Multi-Teacher On-Policy Distillation for Capability-Selectable Flow Models
• Rethinking On-Policy Distillation of Large Language Models II: One Training Example
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Verbalizable Representations Form a Global Workspace in Language Models
• Learn from Whoever Is Right: Answer-Verified Multi-Teacher Distillation for Multi-Domain LLMs
• Measuring Mathematical Problem Solving With the MATH Dataset
• Editing Models with Task Arithmetic
• CA-OPD: Confidence-Aware On-Policy Distillation for Structured Visual Prediction
• PR-OPD: Privileged Representation On-policy Self-Distillation for Agentic Reinforcement Learning
• PHF: Privileged Hidden Flow for On-Policy Self-Distillation
• UI-MOPD: Multi-Platform On-Policy Distillation for Unified GUI Agents
• Recovering General Capabilities via Uncertainty-Calibrated Multi-Teacher On-Policy Distillation
• MOPD: Multi-Teacher On-Policy Distillation for Capability Integration in LLM Post-Training
• Breaking the Tokenizer Barrier: On-Policy Distillation across Model Families
• FitNets: Hints for Thin Deep Nets
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

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