Tower+: Bridging Generality and Translation Specialization in
  Multilingual LLMs
Authors: name: Ricardo Rei, name: Nuno M. Guerreiro, name: José Pombal, name: João Alves, name: Pedro Teixeirinha, name: Amin Farajian, name: André F. T. Martins

Fine-tuning pretrained LLMs has been shown to be an effective strategy for
reaching state-of-the-art performance on specific tasks like machine
translation. However, this process of adaptation often implies sacrificing
general-purpose capabilities, such as conversational reasoning and
instruction-following, hampering the utility of the system in real-world
applications that require a mixture of skills. In this paper, we introduce
Tower+, a suite of models designed to deliver strong performance across both
translation and multilingual general-purpose text capabilities. We achieve a
Pareto frontier between translation specialization and multilingual
general-purpose capabilities by introducing a novel training recipe that builds
on Tower (Alves et al., 2024), comprising continued pretraining, supervised
fine-tuning, preference optimization, and reinforcement learning with
verifiable rewards. At each stage of training, we carefully generate and curate
data to strengthen performance on translation as well as general-purpose tasks
involving code generation, mathematics problem solving, and general
instruction-following. We develop models at multiple scales: 2B, 9B, and 72B.
Our smaller models often outperform larger general-purpose open-weight and
proprietary LLMs (e.g., Llama 3.3 70B, GPT-4o). Our largest model delivers
best-in-class translation performance for high-resource languages and top
results in multilingual Arena Hard evaluations and in IF-MT, a benchmark we
introduce for evaluating both translation and instruction-following. Our
findings highlight that it is possible to rival frontier models in general
capabilities, while optimizing for specific business domains, such as
translation and localization.