Research map / Multi-agent systems and games / LLM agents
Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs
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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
- System 1 (Qwen2.5-7B-Base controller): average accuracy 80.5% vs. Router-R1 69.1% across seven math benchmarks.
- System 2 (Qwen3-4B-Base controller): average accuracy 82.3% vs. Router-R1 66.5%, with the strongest agent used for under 25% of calls.
- Removing Lagrangian multipliers in System 1 drops average accuracy from 80.5% to 56.5% and routes all queries to the strongest agent.
Surprising findings
- Without Lagrangian penalties the controller routes all queries to the strongest agent, showing collapse to the most expensive model.
- Controller verification recall on incorrect responses is about 77-81%, with only 199-216 false rejections.
Limitations
- Experiments limited to math reasoning with verifiable correctness rewards.
- Multi-turn design adds inference latency and compute overhead versus one-shot routing.
- Lagrange multipliers are tuned as hyperparameters; relaxation is not strictly equivalent to the constrained problem.
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
Closest papers by meaning
- Reinforce LLM Reasoning through Multi-Agent Reflection (ICML 2025 · similarity 0.90)
- Selective Critique for Cost-Aware LLM Agents in Long-Horizon Decision Making (NeurIPS 2026 · similarity 0.88)
- From Debate to Equilibrium: Belief‑Driven Multi‑Agent LLM Reasoning via Bayesian Nash Equilibrium (ICML 2025 · similarity 0.88)
- $\texttt{Multi}^2$: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments (ICML 2026 · similarity 0.88)
- HieraMAS: Optimizing Intra-Node LLM Mixtures and Inter-Node Topology for Multi-Agent Systems (ICML 2026 · similarity 0.88)
- Agentic Multi-Turn Reasoning: A Fairness Approach (NeurIPS 2026 · similarity 0.87)
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