ImproveAnyTask: An Autonomous Post-Training Harness for Iterative Model Self-Improvement

2026-10-05

AI Summary

This study addresses the heavy reliance on manual effort and the difficulty of sustained optimization in task adaptation for large language models by proposing an autonomous post-training framework. Inspired by gradient-based optimization, the method precisely identifies model deficiencies through error attribution and instance-level analysis. It then automatically generates training data and configurations via strategy retrieval, achieving closed-loop iterative updates through small-scale validation. Experimental results demonstrate that this framework improves the performance of base and instruct models across eleven tasks by an average of 18.29 and 11.97 percentage points, respectively, with gains reaching up to 41.96 percentage points. These findings establish the proposed approach as an efficient and fully automated solution for the continuous improvement of large language models.

Abstract

Adapting general-purpose large language models to specific tasks requires substantial human effort in designing data and training strategies. Sustaining improvement is especially challenging because model updates change the error distribution, requiring strategies to be continually refined. We introduce ImproveAnyTask, an autonomous post-training harness that improves task performance under a limited compute budget. Drawing inspiration from gradient-based parameter optimization, the harness organizes adaptation into error attribution, update-direction selection, and executable model updates. It combines metric-level and case-level analysis to identify a focal problem, then investigates research-backed strategies and compares their reported gains and reproduction difficulty. The selected strategy is translated into training data and a training configuration, with small-scale execution checks preceding full post-training. Subsequent evaluation guides model selection and further adaptation, while validated strategies and scripts are retained for reuse. Across 11 tasks, ImproveAnyTask achieves mean gains of 18.29 and 11.97 percentage points on the Base and Instruct models, respectively, with a maximum gain of 41.96 points, under a 24-hour budget with resources equivalent to eight H20 GPUs.

Problem

• Large Language Models
• Task Adaptation
• Post-Training
• Model Self-Improvement
• Compute Budget

Innovation

• Autonomous Post-Training
• Iterative Self-Improvement
• Error Attribution
• Strategy Selection
• Compute-Constrained Optimization