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Multi-agent LLM learns to defer to humans using GRPO

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a multi-agent large language model that learns to defer to human input. The model is trained using GRPO on a reward system that accounts for costs, and each instance of deferral is used as supervised fine-tuning data. This allows the model to gradually incorporate human expertise, with a tunable cost parameter enabling a trade-off between accuracy and the budget for human intervention during deployment. AI

IMPACT Introduces a novel training methodology for multi-agent LLMs, enabling adaptive collaboration with human experts.

RANK_REASON The cluster describes a novel research paper detailing a new method for training multi-agent LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — fosstodon.org →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Multi-agent LLM learns to defer to humans using GRPO

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The cluster describes a novel research paper detailing a new method for training multi-agent LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    A multi-agent LLM where each agent learns when to defer to a human, trained with GRPO on a cost-aware reward. Each defer event becomes SFT data, so the model gr

    A multi-agent LLM where each agent learns when to defer to a human, trained with GRPO on a cost-aware reward. Each defer event becomes SFT data, so the model gradually absorbs the human's expertise. Tunable cost knob trades accuracy against human-call budget at deployment, no ret…

    LINKS benjaminhan.net/…/20260520-adaptive-colla…

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