Inference Scaling of LLM Ensembling: Bridging Token Spaces with Token Translation

Large language models (LLMs) exhibit diverse strengths and weaknesses across tasks, motivating recent efforts to ensemble multiple models to harness their complementary capabilities, boosting test-time performance. While model diversity and capability are known to influence ensemble effectiveness, a persistent challenge in LLM ensembling arises from mismatched tokenizer vocabularies. Existing alignment strategies typically rely on token-level embeddings or string-level heuristics of tokens, overlooking the tokenizer priors embedded during LLM pretraining. Specifically, tokenizers such as Byte-Pair Encoding (BPE) and Unigram are constructed by statistically analyzing large pretraining corpora to identify frequent subword units, and they tokenize text using greedy or probabilistic algorithms that reflect these learned subword distributions. In this work, we propose a novel and remarkably simple Token Translation (ToT) method that explicitly leverages these tokenizer priors to bridge heterogeneous token spaces. Our method is lightweight, requiring only a few lines of code, pre-computable, and highly efficient at inference. To further enhance robustness, we incorporate token-level model uncertainty to dynamically reweight each model’s contribution during decoding. Extensive evaluations across diverse model combinations and tasks demonstrate that our method consistently outperforms existing ensembling baselines.