mlsanityData to 14 Oct 2026

ViCuR: Visual Cues as Recoverable Privilege for Multimodal On-Policy Distillation

arXiv 2606.05718 ↗First posted 4 Jun 2026cs.CV cs.AI cs.LGcited by 12

On the map: Knowledge distillation → Multimodal distillation

Abstract (arXiv)

On-policy distillation (OPD) improves reasoning by training a student on trajectories sampled from its own policy under supervision from a teacher. In multimodal reasoning, a common extension is to use a privileged teacher that observes training-time-only signals such as reference answers or rationales. However, such answer-side privilege creates a train-test mismatch: the teacher's supervision may depend on signals unavailable to the student, encouraging shortcut imitation rather than visually grounded reasoning. We propose ViCuR, a visually grounded privileged-teacher distillation framework that replaces answer-side privilege with visual cues (query-related evidence in the input). Because these cues are derived from the same visual input available at inference, their evidence is recoverable by the student. To support this, ViCuR introduces a lightweight cue recovery module that uses dedicated sink-token cross-attention during prefill to aggregate task-relevant visual evidence into an internal representation, without changing the inference interface or requiring auxiliary cue-generation losses. Across seven benchmarks with Qwen3-VL-2B and 8B students, ViCuR consistently improves over answer-based on-policy self-distillation by +1.19 and +1.24 on overall average performance. It also extends naturally to stronger-teacher OPD, surpassing OPD baselines by +0.64 and +1.08, with consistent out-of-domain gains at the 8B scale. These results show that, in multimodal on-policy distillation, the design of teacher privilege is as important as teacher strength.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Placed in Multimodal distillation, one of 12 directions in Knowledge distillation. Builds on 37 of its 39 references that are in the map. First posted on arXiv 4 Jun 2026.

Builds on

37 of 39 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation18 May
  2. 2026VISD: Enhancing Video Reasoning via Structured Self-Distillation7 May
  3. 2026Video-OPD: Efficient Post-Training of Multimodal Large Language Models for Temporal Video Grounding via On-Policy Distillation3 Feb

Other references · 34

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  4. 2025Qwen3-VL Technical Reportcited by 2,793
  5. 2016Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringcited by 2,516
  6. 2023Efficient Streaming Language Models with Attention Sinkscited by 2,186
  7. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 2,057
  8. 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contextscited by 1,614
  9. 2022ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoningcited by 1,479
  10. 2024Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysiscited by 1,413
  11. 2024OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problemscited by 1,136
  12. 2023On-Policy Distillation of Language Models: Learning from Self-Generated Mistakescited by 939
22 more
  1. 2024SnapKV: LLM Knows What You are Looking for Before Generationcited by 880
  2. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 737
  3. 2025Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Modelscited by 665
  4. 2024MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?cited by 586
  5. 2024VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Modelscited by 455
  6. 2026Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Modelscited by 455
  7. 2015Policy Distillationcited by 440
  8. 2021Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoningcited by 383
  9. 2026Reinforcement Learning via Self-Distillationcited by 332
  10. 2026Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipecited by 299
  11. 2024We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?cited by 286
  12. 2021GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoningcited by 228
  13. 2025InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modelingcited by 211
  14. 2026On-Policy Context Distillation for Language Modelscited by 186
  15. 2026Entropy-Aware On-Policy Distillation of Language Modelscited by 175
  16. 2024DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Modelscited by 146
  17. 2026Self-Distilled RLVRcited by 131
  18. 2023Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Taskscited by 64
  19. 2026CRISP: Compressed Reasoning via Iterative Self-Policy Distillationcited by 58
  20. 2024LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge Distillationcited by 45
  21. 2026Demystifying OPD: Length Inflation and Stabilization Strategies for Large Language Modelscited by 30
  22. 2026Learning from Language Feedback via Variational Policy Distillationcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 12.

12 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Visual-OPSD: Cross-Modal On-Policy Self-Distillation for Efficient Unified Multimodal Reasoning
  2. 2026Seeing Before Reasoning: Decoupling Perception and Reasoning for Shortcut-Resilient Multimodal On-Policy Self-Distillation
  3. 2026Teaching the Way, Not the Answer: Privileged Tutoring Distillation for Multimodal Policy Optimization
  4. 2026LEGO-OPD: Factorized Teacher Composition for Multimodal On-Policy Distillation
  5. 2026OPD-V: Visual On-Policy Self-Distillation with Modality Balance
  6. 2026V-Zero: Answer-Label-Free On-Policy Distillation with Contrastive Evidence Gating for Fine-Grained Visual Reasoning
  7. 2026Beyond Absolute Imitation: Anchored Residual Guidance for Privileged On-Policy Distillation
  8. 2026Distilling Visual Reasoning into Text Space