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This research addresses the critical bottleneck of human-in-the-loop systems by introducing Consultation Value Distillation (CVD), a method that optimizes when LLM agents should seek human input based on executor-specific needs. By distilling consultation values offline, the approach enables efficient budgeted allocation without requiring costly real-time rollouts during deployment. The study demonstrates significant improvements in decision capacity management across various batch and sequential settings, offering a practical framework for scaling AI agents that rely on non-scalable human judgment.
Learning When Human Decisions Matter: Executor-Relative Consultation Value for LLM Agents
arXiv:2611.05814v1 Announce Type: cross Abstract: Large language models increasingly automate complex tasks, but human decision capacity does not scale with their capabilities. In many applications, humans remain essential for providing private context, authority, or judgments unavailable to the agent. We formulate human decision capacity allocation: given multiple consultation opportunities and a limited budget, at which decisions should an LLM agent consult a human? The central challenge is estimating consultation value, which is executor-dependent because the same human input may benefit different downstream agents differently. We introduce Consultation Value Distillation (CVD), which estimates consultation values offline using Monte Carlo executor rollouts and distills these values into lightweight predictors for budgeted allocation, requiring no rollout-based value estimation at deployment. Across five batch and three sequential settings with two frontier executors, CVD improves allocation through joint and continuation-aware value learning, while independent consultation values remain competitive in several settings. CVD also broadly outperforms direct prompting and Value of Information where applicable, remains robust with substantially less rollout supervision, and enables nearly lossless approximate allocation at low search cost.
Abstract:Large language models increasingly automate complex tasks, but human decision capacity does not scale with their capabilities. In many applications, humans remain essential for providing private context, authority, or judgments unavailable to the agent. We formulate human decision capacity allocation: given multiple consultation opportunities and a limited budget, at which decisions should an LLM agent consult a human? The central challenge is estimating consultation value, which is executor-dependent because the same human input may benefit different downstream agents differently. We introduce Consultation Value Distillation (CVD), which estimates consultation values offline using Monte Carlo executor rollouts and distills these values into lightweight predictors for budgeted allocation, requiring no rollout-based value estimation at deployment. Across five batch and three sequential settings with two frontier executors, CVD improves allocation through joint and continuation-aware value learning, while independent consultation values remain competitive in several settings. CVD also broadly outperforms direct prompting and Value of Information where applicable, remains robust with substantially less rollout supervision, and enables nearly lossless approximate allocation at low search cost.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2611.05814 [cs.CL]
(or arXiv:2611.05814v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.05814 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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