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Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs

NeurIPS 2026 · Multi-agent systems and games › LLM agents · approach: LLM multi-agent decision making · 0 citations (OpenAlex)

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Proposes an RL-trained controller that critiques intermediate drafts, decides to stop or continue, and re-routes across a heterogeneous LLM agent pool, optimized with GRPO under a Lagrangian usage-constrained reward.

Problem

One-shot routing in multi-agent LLM systems gives no feedback loop to critique and refine drafts, and optimizing answer quality alone over-uses the strongest, most expensive agent.

Approach

Casts controller coordination as a finite-horizon MDP where each action both verifies the previous draft and routes to the next agent (or stops), with a composite routing+verification reward and Lagrangian-relaxed per-agent usage penalties optimized by GRPO.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: MATH-lighteval

Benchmarks: MATH-lighteval MATH-500 Omni-MATH MinervaMath AGI-Eval-MATH AMC23 AIME24

Metrics: accuracy

Baselines: Router-R1 RouterDC RoBERTa Router Random Router Controller V1 Controller V2

Builds on: GRPO Router-R1 RouterDC Search-R1

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