Why Solve It Twice? Hierarchical Accumulation of Skills for Transfer-Efficient ML Engineering
arXiv:2606.30911v1 Announce Type: cross Abstract: ML engineering agents waste compute rediscovering known techniques because every competition is a cold start. We present HASTE, a hierarchical multi-agent system that organizes cross-competition knowledge into three scope tiers (global, domain, and competition-specific), each coupled to a matching agent level. An orchestrator coordinates domain specialists and promotes learning between tiers via LLM-driven abstraction. A controlled ablation provides evidence for scoped loading: holding a 159-skill inventory constant across 8 competitions, tiere
The rapid proliferation of AI agents and the increasing computational demands of ML engineering are driving the need for more efficient and intelligent automation solutions.
This research outlines a hierarchical approach to ML engineering that could dramatically reduce compute waste and accelerate AI development by enabling agents to efficiently reuse knowledge.
ML engineering shifts from a 'cold start' for every project to a more cumulative, knowledge-transfer-driven process, potentially making AI development faster and less resource-intensive.
- · AI developers
- · Cloud compute providers (efficiency gains)
- · AI startups
- · Large language model (LLM) developers
- · ML engineering teams with highly siloed knowledge
- · Companies unable to adopt advanced agentic tooling
ML engineering becomes significantly more efficient, reducing the time and cost to develop new AI models.
Accelerated AI development leads to a faster pace of innovation across various industries, creating new products and services.
The reduced compute burden for training AI models could marginally alleviate pressure on the compute supply chain and energy grid, though overall demand may still rise.
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Read at arXiv cs.LG