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Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents

This story is from 2026-10-06. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2610.02330v1 Announce Type: new Abstract: Large language models (LLMs) rely on long-horizon tool invocation sequences for complex tasks, where each invocation can alter the task state and condition subsequent decisions. In long-horizon tool use, final-outcome rewards provide weak credit assig…

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  1. 2026-10-06 04:00 · arXiv cs.AI
    Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents

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