Learning what to skip: counterfactual credit assignment for efficient multi-agent LLM workflows
Read the original at arxiv.org→arXiv:2609.30734v1 Announce Type: new Abstract: Multi-agent LLM workflows use planning, execution, verification, and summarization to improve task performance, yet the value of each component depends on the state...
Original headline: "Learning What to Skip: Counterfactual Credit Assignment for Efficient Multi-Agent LLM Workflows"
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- Sep 28, 04:00 UTC arXiv cs.AI lead source Learning What to Skip: Counterfactual Credit Assignment for Efficient Multi-Agent LLM Workflows