EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation

Key points

• Complex and long-horizon agent tasks often rely on frontier models deployed in the cloud to achieve strong performance.1
• In this paper, we find that allocating the LLM to only a small subset of key steps can achieve performance comparable to full LLM execution while significantly reducing inference cost.1
• To enable autonomous delegation without additional models at inference time, we introduce a step-level RL method that jointly optimizes task solving and delegation, with fine-grained credit assignment for individual delegation decisions.1
• We also introduce an external judge-based delegation strategy and use it to systematically investigate key design choices in edge-cloud collaboration, including when to delegate, how delegation decisions should be made, what context should be shared, and how frequently the cloud LLM should be invoked.1

Sources (2)

• [1]EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 01:32 AM

  Motivated by this observation, we propose EdgeWise, an edge-cloud collaboration framework that deploys an SLM on the local device as the primary solver and selectively delegates critical steps to a cloud LLM when stronger model capacity is needed.

  Complex and long-horizon agent tasks often rely on frontier models deployed in the cloud to achieve strong performance.

• [2]EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation
  Hugging Face Daily Papers · Oct 13, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.