@inproceedings{yu-etal-2026-ade,
title = "{ADE}: Agentic Data Evolution Framework for Human-Centered Objectives",
author = "Yu, Yang and
Jiang, Yilin and
Fei, Zexuan and
Luo, Yiming and
Song, Xingkai and
Huang, Kaiyi and
Zhou, Aimin and
Lin, Xin and
Tan, Fei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.854/",
pages = "19789--19812",
ISBN = "979-8-89176-459-0",
abstract = "Aligning large language models to human-centered objectives is difficult when targets are non-executable and context-dependent, limiting reliable verification and scalable supervision. Although synthetic data expands coverage, weak verification shifts the bottleneck from generation to selection. Noisy signals destabilize iterative refinement and can cause silent regressions. We propose Agentic Data Evolution (ADE), a data-centric framework that organizes synthetic supervision as evolving data snapshots. ADE improves data snapshots through a closed-loop Observation-Variation-Selection (OVS) procedure, where a steady-state admission mechanism acts as a quality ratchet that conservatively gates updates for sustained cross-round improvement. We validate these improvements through complementary intrinsic trend tracking and extrinsic post-training evaluation. On DEV300, ADE raises the intrinsic win rate from 50{\%} to 75.81{\%} and the extrinsic win rate from 55.20{\%} to 68.86{\%}, consistent performance gains across diverse benchmarks. Blind expert evaluation further confirms this, with a 66.11{\%} preference for evolved answers. These gains generalize effectively across various model scales, post-training methods, and beyond weakly verifiable objectives. Resources are available at https://github.com/ZeroLoss-Lab/Agentic-Data-Evolution."
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<abstract>Aligning large language models to human-centered objectives is difficult when targets are non-executable and context-dependent, limiting reliable verification and scalable supervision. Although synthetic data expands coverage, weak verification shifts the bottleneck from generation to selection. Noisy signals destabilize iterative refinement and can cause silent regressions. We propose Agentic Data Evolution (ADE), a data-centric framework that organizes synthetic supervision as evolving data snapshots. ADE improves data snapshots through a closed-loop Observation-Variation-Selection (OVS) procedure, where a steady-state admission mechanism acts as a quality ratchet that conservatively gates updates for sustained cross-round improvement. We validate these improvements through complementary intrinsic trend tracking and extrinsic post-training evaluation. On DEV300, ADE raises the intrinsic win rate from 50% to 75.81% and the extrinsic win rate from 55.20% to 68.86%, consistent performance gains across diverse benchmarks. Blind expert evaluation further confirms this, with a 66.11% preference for evolved answers. These gains generalize effectively across various model scales, post-training methods, and beyond weakly verifiable objectives. Resources are available at https://github.com/ZeroLoss-Lab/Agentic-Data-Evolution.</abstract>
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%0 Conference Proceedings
%T ADE: Agentic Data Evolution Framework for Human-Centered Objectives
%A Yu, Yang
%A Jiang, Yilin
%A Fei, Zexuan
%A Luo, Yiming
%A Song, Xingkai
%A Huang, Kaiyi
%A Zhou, Aimin
%A Lin, Xin
%A Tan, Fei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yu-etal-2026-ade
%X Aligning large language models to human-centered objectives is difficult when targets are non-executable and context-dependent, limiting reliable verification and scalable supervision. Although synthetic data expands coverage, weak verification shifts the bottleneck from generation to selection. Noisy signals destabilize iterative refinement and can cause silent regressions. We propose Agentic Data Evolution (ADE), a data-centric framework that organizes synthetic supervision as evolving data snapshots. ADE improves data snapshots through a closed-loop Observation-Variation-Selection (OVS) procedure, where a steady-state admission mechanism acts as a quality ratchet that conservatively gates updates for sustained cross-round improvement. We validate these improvements through complementary intrinsic trend tracking and extrinsic post-training evaluation. On DEV300, ADE raises the intrinsic win rate from 50% to 75.81% and the extrinsic win rate from 55.20% to 68.86%, consistent performance gains across diverse benchmarks. Blind expert evaluation further confirms this, with a 66.11% preference for evolved answers. These gains generalize effectively across various model scales, post-training methods, and beyond weakly verifiable objectives. Resources are available at https://github.com/ZeroLoss-Lab/Agentic-Data-Evolution.
%U https://aclanthology.org/2026.emnlp-main.854/
%P 19789-19812
Markdown (Informal)
[ADE: Agentic Data Evolution Framework for Human-Centered Objectives](https://aclanthology.org/2026.emnlp-main.854/) (Yu et al., EMNLP 2026)
ACL
- Yang Yu, Yilin Jiang, Zexuan Fei, Yiming Luo, Xingkai Song, Kaiyi Huang, Aimin Zhou, Xin Lin, and Fei Tan. 2026. ADE: Agentic Data Evolution Framework for Human-Centered Objectives. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 19789–19812, Budapest, Hungary. Association for Computational Linguistics.