mlsanityData to 14 Oct 2026

Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillation

arXiv 2610.13521 ↗First posted 9 Oct 2026cs.AIcited by 0

On the map: Knowledge distillation → On-policy and self-distillation for LLMs

Abstract (arXiv)

Multi-teacher on-policy distillation (MOPD) extends on-policy distillation (OPD) by routing domain-specific data to specialized teachers, integrating their capabilities through dense token-level supervision on student-generated trajectories. However, domain routing only distinguishes the supervision sources for different domains; updates from all domains still act on shared parameters. When teachers differ substantially in response style and domain expertise, supervision that is effective in isolation may still lead to persistent gradient conflicts and capability degradation during joint optimization. To address this challenge, we propose Cross-Teacher Guarding for MOPD (Road MOPD), which introduces non-routed teachers as auxiliary cross-domain critics on the same student trajectories and coordinates multi-teacher supervision through token-level credit assignment and gradient updates. Specifically, cross-teacher-guided credit assignment uses teacher agreement to strengthen token-level corrections jointly supported by the teachers and attenuate conflicting signals; preventive multi-teacher gradient projection uses auxiliary gradients from the same trajectories to constrain potentially conflicting updates and adaptively controls projection strength to retain target-domain learning signals. In experiments, Road MOPD mitigates degradation in the disadvantaged domain, gains over 5 percentage points under high conflict, and improves overall performance in low-conflict and three-domain settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in On-policy and self-distillation for LLMs, one of 12 directions in Knowledge distillation. Builds on 38 of its 46 references that are in the map. First posted on arXiv 9 Oct 2026.

Builds on

38 of 46 references.

Closest ancestors · 15

Same direction, newest first

  1. 2026A Token-Level Analysis of Sampled-Token Reverse-KL On-Policy Distillation26 Aug
  2. 2026D3-MOPD: Dynamic Domain ScheDuling for Efficient Multi-Teacher Distillation25 Aug
  3. 2026On-policy Distillation with Verifiable Reward25 Aug
  4. 2026Beyond Imitation: Filtering On-Policy Distillation by Reasoning Progress19 Aug
  5. 2026Beyond Teacher Likelihood: Group-Calibrated On-Policy Distillation for Long-Context Reasoning19 Aug
  6. 2026Open-MOPD: Diagnosing and Fixing Capability Imbalance in Multi-Teacher On-Policy Distillation19 Aug
  7. 2026Every Coin Has Two Sides: On the Dual Nature of Generalization in On-Policy Distillation of Large Language Models17 Aug
  8. 2026Step-Level On-Policy Distillation: Interpolating Between On-Policy Distillation and Supervised Fine-Tuning17 Aug
  9. 2026SimpleOPD: Simple Tokenizer-Agnostic On-Policy Distillation for Long-Context Reasoning14 Aug
  10. 2026MOPD: Multi-Teacher On-Policy Distillation for Capability Integration in LLM Post-Training29 Jun
  11. 2026Filter, Then Reweight: Rethinking Optimization Granularity in On-Policy Distillation1 Jun
  12. 2026Counteraction-Aware Multi-Teacher On-Policy Distillation for General Capability Recovery with Domain Preservation26 May
3 more
  1. 2026Uni-OPD: Unifying On-Policy Distillation with a Dual-Perspective Recipe5 May
  2. 2026Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation19 Mar
  3. 2026Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation12 Feb

Other references · 23

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2025Qwen3 Technical Reportcited by 8,820
  4. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 2,004
  5. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
  6. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
  7. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  8. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 1,040
  9. 2023On-Policy Distillation of Language Models: Learning from Self-Generated Mistakescited by 939
  10. 2016Sequence-Level Knowledge Distillationcited by 801
  11. 2022MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answeringcited by 543
  12. 2025Process Reinforcement through Implicit Rewardscited by 504
11 more
  1. 2021Conflict-Averse Gradient Descent for Multi-task Learningcited by 459
  2. 2023DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretrainingcited by 399
  3. 2025DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoningcited by 280
  4. 2024HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMscited by 265
  5. 2025MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understandingcited by 224
  6. 2026MiMo-V2-Flash Technical Reportcited by 191
  7. 2025MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphscited by 110
  8. 2025DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMscited by 84
  9. 2025m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning with Large Language Modelscited by 46
  10. 2025MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learningcited by 30
  11. 2026H-OPD: Confidence Aware Heterogeneous Multi-Teacher Multimodal On-policy Distillationcited by 9

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  2. 2026Composing What Each Teacher Learned: Multi-Teacher On-Policy Distillation through Teacher-Relative Shifts
  3. 2026Distill What You Trust: Reliability-Aware Multi-Teacher On-Policy Distillation
  4. 2026From Gradients to Capabilities: Understanding Multi-Teacher On-Policy Distillation
  5. 2026No Pain, More Gain: Iterative Merging for Effective Multi-Teacher On-Policy Distillation
  6. 2026When Teachers Mislead: Spurious-Signal-Aware On-Policy Distillation
  7. 2026On the Off-Policy Teacher in On-Policy Distillation
  8. 2026SimCT: Recovering Lost Supervision for Cross-Tokenizer On-Policy Distillation