DLLG: Dynamic Logit-Level Gating of LLM Experts
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Computer Science > Computation and Language
Title:DLLG: Dynamic Logit-Level Gating of LLM Experts
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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.04378 [cs.CL] |
| (or arXiv:2606.04378v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.04378
arXiv-issued DOI via DataCite (pending registration)
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