CERA-MoA: Co-Evolving Routing Mechanisms with Continually Learning LLM Agents
CERA-MoA is a reinforcement learning framework where query routing and agent fine-tuning co-evolve, outperforming static Mixture-of-Agents routing baselines.
The paper (arXiv 2609.18779) introduces CERA-MoA, an iterative reinforcement learning framework in which a dynamic query router and independent agent policies co-evolve during post-training. A predictive familiarity estimator uses mid-layer hidden states to score agent competence without full rollouts, and cumulative-threshold adaptive routing activates a minimal tailored agent subset. Experiments across various domains show it outperforms state-of-the-art static-agent routing and fixed-workflow fine-tuning baselines.
- Predictive familiarity estimator uses mid-layer hidden states to score agent competence, avoiding full rollout overhead.
- Cumulative-threshold adaptive routing activates only a minimal tailored agent subset, balancing performance and efficiency.
- Targeted training-sample allocation based on evolving competence drives capability differentiation among agents.
From the original article
1 min read at huggingface.co
Current Mixture-of-Agents (MoA) paradigms generally treat query routing and agent fine-tuning as separate processes, limiting their ability to respond to evolving agent capabilities. This disconnect prevents routing strategies from adapting to evolving agent capabilities during post-training and prevents agents from achieving synergistic data-driven specialization.
By Jiaxuan Jiang, Liyuan He, Zhixuan Fang · huggingface.co
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