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ImproveAnyTask introduces an autonomous framework that automates the post-training process for large language models, significantly reducing human effort in data design and strategy selection. By systematically analyzing errors and executing targeted updates within a constrained compute budget, the system achieves substantial performance gains across diverse tasks without requiring full-scale retraining. This approach addresses the critical challenge of sustaining model improvement as error distributions shift after each update cycle.
ImproveAnyTask: An Autonomous Post-Training Harness for Iterative Model Self-Improvement
arXiv:2610.06347v1 Announce Type: new 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.
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.
Artificial Intelligence (cs.AI)
arXiv:2610.06347 [cs.AI]
(or arXiv:2610.06347v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.06347 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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