🔬Learning What to Skip: Cutting Tokens in Multi-Agent Flows
TL;DR
LW2S trains a model to skip workflow steps that add cost but no accuracy. Across math, multiple-choice and code tasks it cut recorded token cost while matching or beating the full workflow.
LW2S trains a model to skip workflow steps that add cost but no accuracy. Across math, multiple-choice and code tasks it cut recorded token cost while matching or beating the full workflow. Agent agreement alone is a poor skip signal.
Key Points
Submitted Sept 25, 2026 by Xu, Chen, Peng and colleagues, senior author Victor Leung
Uses counterfactual intervention data to learn when a step is safe to omit
Combines calibration with domain-specific safeguards
Rejected skips can be reconsidered at later stages
Finding: component agreement cannot reliably predict when skipping is safe
Why It Matters
Multi-agent pipelines often re-run verifiers that overwrite correct answers. If you pay per token, learned skipping is a cheaper fix than shrinking the workflow by hand.
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