LEGO-OPD: Factorized Teacher Composition for Multimodal On-Policy Distillation
Quick summary
arXiv:2610.00333v1 Announce Type: cross Abstract: Multimodal on-policy distillation (OPD) aims to improve visual grounding while preserving the strong reasoning capabilities of language models. Recent multi-teacher approaches combine LLM and VLM teachers to provide complementary supervision. However, directly using a VLM's full predictive distribution entangles its visual grounding signal with its own language prior, preventing the grounding information from being transferred independently. Conversely, increasing the strength of visual supervision can improve perception but may overemphasize v
Key takeaways
- arXiv:2610.00333v1 Announce Type: cross Abstract: Multimodal on-policy distillation (OPD) aims to improve visual grounding while preserving the strong reasoning capabilities of language models.
- Recent multi-teacher approaches combine LLM and VLM teachers to provide complementary supervision.
- However, directly using a VLM's full predictive distribution entangles its visual grounding signal with its own language prior, preventing the grounding information from being transferred independently.
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “LEGO-OPD: Factorized Teacher Composition for Multimodal On-Policy Distillation” may reshape data collection, model training, output accountability and market access.
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