Pass the Baton: Trajectory-Relayed On-Policy Distillation
On the map: Knowledge distillation → On-policy and self-distillation for LLMs
Abstract (arXiv)
On-policy distillation (OPD) grounds token-level supervision in the student's own trajectory, yet suffers from prefix failure: once the student commits to a wrong reasoning direction, all subsequent generation builds on this deviation, producing misdirected continuations that elicit unreliable supervision and waste compute. We identify a teacher-student continuation asymmetry on failed prefixes, where the teacher tends to redirect while the student continues along the original direction, and convert it into a label-free handoff trigger in Relay On-Policy Distillation (Relay-OPD). During training, Relay-OPD constructs relay trajectories by letting the teacher briefly take over at detected trigger points to produce a teacher leg, after which the student resumes and is optimized on the resulting trajectory. A limited relay budget concentrates intervention on critical early positions while limiting departure from the student policy. With a Qwen3-4B-Instruct-2507 teacher and Qwen3-0.6B/1.7B-Non-Thinking students on eight mathematical reasoning benchmarks, Relay-OPD achieves the best or second-best results on every benchmark, outperforming standard OPD by +5.73% and the strongest baseline FastOPD by +1.49% on average for 1.7B, with consistent gains at 0.6B. Training trajectory length is reduced by over 50%.
Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Placed in On-policy and self-distillation for LLMs, one of 12 directions in Knowledge distillation. Builds on 30 of its 32 references that are in the map. First posted on arXiv 28 Jul 2026.
Builds on
30 of 32 references.
Closest ancestors · 9
Same direction, newest first
- 2026On the Position Bias of On-Policy Distillation21 Jun
- 2026Trajectory-Refined Distillation7 Jun
- 2026Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe14 Apr
- 2026Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes26 Mar
- 2026Fast and Effective On-policy Distillation from Reasoning Prefixes16 Feb
- 2026Privileged Information Distillation for Language Models4 Feb
- 2026Reinforcement Learning via Self-Distillation28 Jan
- 2026Self-Distillation Enables Continual Learning27 Jan
- 2026Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models26 Jan
Other references · 21
- 2015Distilling the Knowledge in a Neural Networkcited by 10,324
- 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
- 2025Qwen3 Technical Reportcited by 8,820
- 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
- 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
- 2023Let's Verify Step by Stepcited by 3,789
- 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,810
- 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 2,057
- 2022Fast Inference from Transformers via Speculative Decodingcited by 1,653
- 2025s1: Simple test-time scalingcited by 1,341
- 2024OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problemscited by 1,136
- 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 1,040
9 more
- 2023Accelerating Large Language Model Decoding with Speculative Samplingcited by 1,006
- 2023On-Policy Distillation of Language Models: Learning from Self-Generated Mistakescited by 939
- 2016Sequence-Level Knowledge Distillationcited by 801
- 2026GLM-5: from Vibe Coding to Agentic Engineeringcited by 587
- 2025MathArena: Evaluating LLMs on Uncontaminated Math Competitionscited by 297
- 2026MiMo-V2-Flash Technical Reportcited by 191
- 2024Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Samplingcited by 90
- 2025Black-Box On-Policy Distillation of Large Language Modelscited by 59
- 2023Distilling Rule-based Knowledge into Large Language Modelscited by 9
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Citing papers per year: 2026: 18.
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- 2026Look Ahead Before You Distill: Future Trajectory Validation of Teacher Guidance for Agentic On-Policy Distillationcited by 11
- 2026When EOS Tokens Disagree: Understanding Length Inflation in On-Policy Distillationcited by 5
- 2026MInTRL: Off-policy Intervention can boost On-policy RLcited by 0
- 2026OPD-Aha: From Linguistic Momentum to Visual Reflection in Multimodal On-Policy Distillationcited by 0
- 2026Teaching a Moving Student: Rethinking the Curriculum of On-Policy Distillationcited by 0
- 2026Expected Reasoning-Step Return Unifies On-Policy Learning from Rewards and Teacherscited by 0
- 2026Interpolated Policy Distillation: A Controllable Continuum Between Off-Policy and On-Policy Distillationcited by 0
- 2026From Dissonance to Orchestration: Teacher Intervention in On-Policy Distillationcited by 0
- 2026On the Off-Policy Teacher in On-Policy Distillationcited by 0
- 2026Learning from Evolving Errors: Adaptive Iterative Repair for On-Policy Distillationcited by 0
- 2026Learning to Revise Reasoning with Segment-wise On-Policy Distillationcited by 0
- 2026How Should Teachers Be Prepared? RL on Student-Induced States for On-Policy Distillationcited by 0
6 more
- 2026On-Policy Distillation with Negative-Policy Rolloutscited by 0
- 2026When Do We Need On-Policy Distillation? Distilling on Offline Student Rollouts Is Often Bettercited by 0
- 2026MetaOPD: Meta-Learned Token Weighting for On-Policy Distillationcited by 0
- 2026Demystifying On-Policy Distillation: Where Does the Teacher's Advantage Live?cited by 0
- 2026DTDD: Divergence-Triggered Dynamic Distillation for Reliable On-Policy Supervisioncited by 0
- 2026Recovering Reliable Supervision via Critique-Guided On-Policy Distillationcited by 0
- 2026Recovering Reliable Supervision via Critique-Guided On-Policy Distillation14 Oct 2026
- 2026DTDD: Divergence-Triggered Dynamic Distillation for Reliable On-Policy Supervision13 Oct 2026
- 2026Demystifying On-Policy Distillation: Where Does the Teacher's Advantage Live?13 Oct 2026
- 2026MetaOPD: Meta-Learned Token Weighting for On-Policy Distillation8 Oct 2026
- 2026When Do We Need On-Policy Distillation? Distilling on Offline Student Rollouts Is Often Better8 Oct 2026
- 2026On-Policy Distillation with Negative-Policy Rollouts6 Oct 2026
- 2026How Should Teachers Be Prepared? RL on Student-Induced States for On-Policy Distillation4 Oct 2026
- 2026Learning to Revise Reasoning with Segment-wise On-Policy Distillation2 Oct 2026
- 2026Learning from Evolving Errors: Adaptive Iterative Repair for On-Policy Distillation2 Oct 2026
- 2026On the Off-Policy Teacher in On-Policy Distillation29 Sep 2026
- 2026From Dissonance to Orchestration: Teacher Intervention in On-Policy Distillation29 Sep 2026
- 2026Interpolated Policy Distillation: A Controllable Continuum Between Off-Policy and On-Policy Distillation29 Sep 2026
6 more
- 2026Expected Reasoning-Step Return Unifies On-Policy Learning from Rewards and Teachers26 Sep 2026
- 2026When EOS Tokens Disagree: Understanding Length Inflation in On-Policy Distillation17 Sep 2026
- 2026OPD-Aha: From Linguistic Momentum to Visual Reflection in Multimodal On-Policy Distillation15 Sep 2026
- 2026MInTRL: Off-policy Intervention can boost On-policy RL11 Sep 2026
- 2026Teaching a Moving Student: Rethinking the Curriculum of On-Policy Distillation6 Sep 2026
- 2026Look Ahead Before You Distill: Future Trajectory Validation of Teacher Guidance for Agentic On-Policy Distillation3 Aug 2026
- 2026When EOS Tokens Disagree: Understanding Length Inflation in On-Policy Distillationcited by 5
- 2026Teaching a Moving Student: Rethinking the Curriculum of On-Policy Distillationcited by 0
- 2026Interpolated Policy Distillation: A Controllable Continuum Between Off-Policy and On-Policy Distillationcited by 0
- 2026From Dissonance to Orchestration: Teacher Intervention in On-Policy Distillationcited by 0
- 2026On the Off-Policy Teacher in On-Policy Distillationcited by 0
- 2026On-Policy Distillation with Negative-Policy Rolloutscited by 0
- 2026When Do We Need On-Policy Distillation? Distilling on Offline Student Rollouts Is Often Bettercited by 0
- 2026MetaOPD: Meta-Learned Token Weighting for On-Policy Distillationcited by 0
- 2026Demystifying On-Policy Distillation: Where Does the Teacher's Advantage Live?cited by 0
- 2026DTDD: Divergence-Triggered Dynamic Distillation for Reliable On-Policy Supervisioncited by 0
- 2026Recovering Reliable Supervision via Critique-Guided On-Policy Distillationcited by 0
- 2026Recovering Reliable Supervision via Critique-Guided On-Policy Distillation14 Oct 2026
- 2026DTDD: Divergence-Triggered Dynamic Distillation for Reliable On-Policy Supervision13 Oct 2026
- 2026Demystifying On-Policy Distillation: Where Does the Teacher's Advantage Live?13 Oct 2026
- 2026MetaOPD: Meta-Learned Token Weighting for On-Policy Distillation8 Oct 2026
- 2026When Do We Need On-Policy Distillation? Distilling on Offline Student Rollouts Is Often Better8 Oct 2026
- 2026On-Policy Distillation with Negative-Policy Rollouts6 Oct 2026
- 2026On the Off-Policy Teacher in On-Policy Distillation29 Sep 2026
- 2026From Dissonance to Orchestration: Teacher Intervention in On-Policy Distillation29 Sep 2026
- 2026Interpolated Policy Distillation: A Controllable Continuum Between Off-Policy and On-Policy Distillation29 Sep 2026
- 2026When EOS Tokens Disagree: Understanding Length Inflation in On-Policy Distillation17 Sep 2026
- 2026Teaching a Moving Student: Rethinking the Curriculum of On-Policy Distillation6 Sep 2026
- 2026OPD-Aha: From Linguistic Momentum to Visual Reflection in Multimodal On-Policy Distillationcited by 0
- 2026Expected Reasoning-Step Return Unifies On-Policy Learning from Rewards and Teacherscited by 0
- 2026Learning from Evolving Errors: Adaptive Iterative Repair for On-Policy Distillationcited by 0
- 2026Learning to Revise Reasoning with Segment-wise On-Policy Distillationcited by 0
- 2026How Should Teachers Be Prepared? RL on Student-Induced States for On-Policy Distillationcited by 0
- 2026How Should Teachers Be Prepared? RL on Student-Induced States for On-Policy Distillation4 Oct 2026
- 2026Learning to Revise Reasoning with Segment-wise On-Policy Distillation2 Oct 2026
- 2026Learning from Evolving Errors: Adaptive Iterative Repair for On-Policy Distillation2 Oct 2026
- 2026Expected Reasoning-Step Return Unifies On-Policy Learning from Rewards and Teachers26 Sep 2026
- 2026OPD-Aha: From Linguistic Momentum to Visual Reflection in Multimodal On-Policy Distillation15 Sep 2026
- 2026MInTRL: Off-policy Intervention can boost On-policy RLcited by 0
- 2026MInTRL: Off-policy Intervention can boost On-policy RL11 Sep 2026
Similar, not cited
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- 2026TISD: On-Policy Self-Distillation with Trajectory Intervention
- 2026Prefix-Guided On-Policy Distillation: Mining Golden Trajectories from Rollouts
- 2026Branching On-Policy Distillation
- 2026ReOrder-OPD:Reliability-Aware Prompt Ordering for On-Policy Distillation
- 2026Spend Teacher Tokens Where They Matter: Success-Referenced On-Policy Distillation
- 2026Reward-Gated On-Policy Distillation
- 2026What Shared Prefixes Hide: Trajectory Dropout for On-Policy Distillation
- 2026SG-OPD: Sign-Gated On-Policy Distillation via Sign-Consistency Gating and Phased Teacher Sampling