Budgeted agentic AI for adaptive lightweight model fine-tuning

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Abstract
On-device deployment often relies on lightweight models that require fine-tuning when deployment environments differ from development conditions or when model performance changes over time. Although recent LLM-enabled agents are capable of reasoning about system states and forming adaptive strategies, they often incur substantial computational and budgetary overhead, making them impractical for resource-constrained edge settings. This paper presents Budgeted Agentic AI for Adaptive Lightweight Model Fine-Tuning (BA2), a framework for budget-constrained lightweight model adaptation that combines bounded optimization steps with auxiliary evaluation, rollback, and compression actions. BA2 enables agents to dynamically adjust adaptation strategies according to real-time budget conditions while maximizing system performance. BA2 explicitly accounts for constrained resources, including limited fine-tuning steps, tool invocation quotas, and token consumption. An Engineer–Manager architecture is introduced, where an LLM-based Engineer generates parameterized candidate operations from system logs, and a lightweight contextual-bandit Manager selects actions conditioned on both environmental states and remaining budgets. Experimental results demonstrate that BA2 achieves superior cost-performance trade-offs compared with static heuristics and inference-time search agents under tight budget constraints, while remaining competitive with strong baselines in higher-budget regimes.
Funding Information
  • Postdoctoral Fellowship Program of CPSF (2025M781639)
  • Postdoctoral Fellowship Program of CPSF (2025T180476)
  • Postdoctoral Fellowship Program of CPSF (GZB20250432)
  • National Natural Science Foundation of China (62503177)
  • National Natural Science Foundation of China (62573197)
  • Natural Science Foundation of Shanghai Municipality (24ZR1416400)
  • Shanghai Baiyulan Talent Program Pujiang Project (24PJD020)
  • Industry-Academia-Research Collaboration Fund of the Eighth Academy of China Aerospace Science and Technology Corporation (SAST2024-060)
  • Shanghai Science and Intelligence “Hundred Teams, Hundred Projects” (RZ-RGZN-01-25-0951)

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