AgentBaton enables cost-effective model collaboration in agentic tasks
Original title: AgentBaton: Efficient Model Collaboration in Agentic Tasks
Capable-to-efficient handoffs in AgentBaton reduce API costs by 65.9% while preserving or improving accuracy on long-horizon agentic tasks.
Long-horizon agentic tasks require interaction with environments over multiple steps. AgentBaton investigates model handoffs between capable cloud models and efficient local models. Capable-to-efficient handoffs at suitable task stages preserve accuracy and cut costs. A lightweight detector learns when to hand off from task progress patterns across 11 model pairs and two benchmarks.
- Average cost reduction of 65.9% across experiments
- Accuracy gains up to 8.8 percentage points
- One-time well-timed handoff strategy suffices
- Extends empirical accuracy-cost Pareto frontier
From the original article
1 min read at huggingface.co
Solving long-horizon agentic tasks requires language agents to interact with their environments over multiple steps, and reliable performance often comes from the expensive capable models available through cloud providers. Meanwhile, alternative efficient models with inferior performance but a much lower cost are emerging, including lower-tier cloud models and smaller open-weights models deployable on local hardware.
By Junlong Li, Jindong Jiang, Yonggan Fu · huggingface.co
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