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VASPO: Self-Distillation with Privileged Value Estimation

Paper recorded by Signals 4 on 2026-10-14 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-14 on arXiv · recorded by Signals 4 on 2026-10-15

Category: cs.AI · 人工智能 · first seen 2026-10-15

Abstract

On-policy self-distillation provides dense supervision for reasoning models via a privileged self-teacher. However, this supervision reflects local token preferences rather than the expected return of an unguided student continuation. Recent methods combine this signal with GRPO. They use GRPO for update directions and the teacher for signal scaling. However, we demonstrate a more effective integr

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#318 most recent of 960 cs.AI papers we have recorded · ↑ newer: The Wealth of Agents: Scaling Agentic AI Across Developers · ↓ older: VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution
Cite this page: VASPO: Self-Distillation with Privileged Value Estimation: the #318 most recent of 960 cs.AI papers we have recorded (as of 2026-10-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/vaspo-self-distillation-with-privileged-value-estimation.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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