---
title: "Agent Stack for ML Training Lifecycle: Generation, Validation, and Optimization"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.00384v1"
permalink: "https://feed.astralor.com/items/itm_933473b46c030bc712077cde"
published: "2026-10-30T21:05:50.000Z"
score: 57
channel: "tech"
tags: ["AI Agent","Agent Stack","Training Lifecycle","Generation","Validation","Optimization Production","LinkedIn","Production"]
---

# Agent Stack for ML Training Lifecycle: Generation, Validation, and Optimization

## At a glance

Production model development requires repeated architecture design, platform integration, debugging, training, and evaluation. We present an agent stack deployed at LinkedIn with two independently usable agent loops: Au…

## Key points

- Production model development requires repeated architecture design, platform integration, debugging, training, and evaluation.
- We present an agent stack deployed at LinkedIn with two independently usable agent loops: AutoTorch for model generation and migration, and AutoResearch for model optimization.
- AutoTorch generates PyTorch models and training pipelines for TensorFlow migration, XGBoost replacement, and new-model development.

## Full text

Production model development requires repeated architecture design, platform integration, debugging, training, and evaluation. We present an agent stack deployed at LinkedIn with two independently usable agent loops: AutoTorch for model generation and migration, and AutoResearch for model optimization. AutoTorch generates PyTorch models and training pipelines for TensorFlow migration, XGBoost replacement, and new-model development. Executable validation hooks check platform API compliance, trainability, and conversion fidelity on sampled data, guiding iterative repair before engineer review and full-scale distributed training. AutoResearch explores architecture and training-configuration changes through expert-steered or autonomous experimentation. An external orchestrator manages parallel trials, experiment history, candidate selection, and wall-clock budgets across agent sessions. A shared runtime preserves execution state independently of agent reasoning and allocates GPUs separately from agent execution. Across ranking, content understanding, and recommendation workloads, AutoTorch completed generation and full-scale training in 92 of 115 workflow runs (80%). Of these, 77 (83.7%) produced models meeting post-training quality requirements. In a separate cohort, AutoResearch improved the primary task metric on held-out data for 28 of 100 production pipelines. Engineering teams estimate that the stack reduced elapsed end-to-end migration time from approximately eight weeks to two weeks, a 4x speedup.

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