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Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with Synthetic Scenes

Paper recorded by Signals 4 on 2026-10-01 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-01 on arXiv · recorded by Signals 4 on 2026-10-02

Category: cs.AI · 人工智能 · first seen 2026-10-02

Abstract

On-policy self-distillation has recently emerged as an effective approach for improving language-model reasoning by supervising students with a frozen or EMA version of themselves that receives privileged information. Its application to multimodal large language models (MLLMs), however, remains largely unexplored. Recent approaches use privileged visual information, such as image crops correspondi

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#20 most recent of 500 cs.AI papers we have recorded · ↑ newer: Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows · ↓ older: Semifactual Credit-Augmented Policy Optimization
Cite this page: Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with Synthetic Scenes: the #20 most recent of 500 cs.AI papers we have recorded (as of 2026-10-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/where-opd-spatially-guided-on-policy-self-distillation-of-mllms-with-synthetic-s.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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