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AdviSD: Learning to Advise Frontier LLMs via Targeted Multi-Turn Self-Distillation

Paper recorded by Signals 4 on 2026-09-29 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-29 on arXiv · recorded by Signals 4 on 2026-09-30

Category: cs.AI · 人工智能 · first seen 2026-09-30

Abstract

A small trainable advisor can steer a frozen language-model executor using natural-language advice. In addition to learning from task rewards, the advisor can use feedback from completed interactions to improve its advice. However, a plausible correction need not change execution, yet learning from such corrections can still affect the advisor's future decisions in other contexts. In a shared-para

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#7 most recent of 460 cs.AI papers we have recorded · ↑ newer: Learning Meta-Skills for Agent Harness Design in Test-Time AI4AI · ↓ older: Breaking the Uniformity Trap: Scaling Video Diffusion Model via SplitM
Cite this page: AdviSD: Learning to Advise Frontier LLMs via Targeted Multi-Turn Self-Distillation: the #7 most recent of 460 cs.AI papers we have recorded (as of 2026-09-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/advisd-learning-to-advise-frontier-llms-via-targeted-multi-turn-self-distillatio.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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