---
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id: "554e0ae150d4ac641f1da26ffd8c53c4327f4cf922cb63fb0580d7dfc2a5382e"
canonical_url: "https://aidr.today/554e0ae1?lang=en"
title: "SteerDistill: Quantizing LLM Agents via Closed-Loop Teacher Steering"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
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published_at: "2026-11-12T00:00:00.000Z"
category: "Research"
topics: ["agent"]
source_urls: ["https://huggingface.co/papers/2611.13410"]
summary: "SteerDistill is a quantization-aware distillation method where a full-precision teacher guides a student to revise failed responses. Across five agentic benchmarks and three models, SteerDistill exceeds the strongest quantized baseline by 1.67--2.18 points in average score under W4A16."
---

# SteerDistill: Quantizing LLM Agents via Closed\-Loop Teacher Steering

> [Open the canonical story](<https://aidr.today/554e0ae1?lang=en>)

**Published:** 2026-11-12T00:00:00.000Z
**Category:** Research
**Topics:** agent

## Summary

SteerDistill is a quantization\-aware distillation method where a full\-precision teacher guides a student to revise failed responses\. Across five agentic benchmarks and three models, SteerDistill exceeds the strongest quantized baseline by 1\.67\-\-2\.18 points in average score under W4A16\.

## Sources

- [Story source](<https://huggingface.co/papers/2611.13410>)