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arXiv · 2606.04378

DLLG: Dynamic Logit-Level Gating of LLM Experts

Bingnan Li·Zhaoyang Zhang·Xiaoze Liu·Yantao Shen·Shuli Jiang·Shuo Yang·Wei Xia·Zhuowen Tu·Stefano Soatto
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Abstract

Leveraging multiple specialized LLMs can combine complementary strengths, but existing approaches trade adaptability for stability: routing commits prematurely, heuristic ensembling depends on fragile proxies, and parameter merging introduces interference. We propose DLLG (Dynamic Logit-Level Gating), a dynamic logit-level ensembling framework that learns token-level expert fusion from sparse response-level supervision. A lightweight gating module predicts step-wise fusion weights, linking trajectory-level correctness to generation without token-level labels or expert retraining. Across diverse reasoning and code benchmarks, DLLG consistently outperforms strong routing, heuristic ensembling, and parameter-merging baselines across model scales, highlighting learned logit-level fusion as a robust and scalable paradigm for integrating specialized experts.

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Bingnan Li, Zhaoyang Zhang, Xiaoze Liu, Yantao Shen, Shuli Jiang, Shuo Yang, Wei Xia, Zhuowen Tu, Stefano Soatto. 2026-06-03. DLLG: Dynamic Logit-Level Gating of LLM Experts. https://arxiv.org/abs/2606.04378

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