BAR (Branch-Adapt-Route) is a modular post-training approach that extends a fully post-trained language model with new domain capabilities via independently trained Mixture-of-Experts. Rather than retraining a single model across all domains, BAR trains independent domain experts — each through its own mid-training, supervised finetuning (SFT), and reinforcement learning pipeline — and composes them into a unified model via an MoE architecture with lightweight router training. — BAR-7B — initial fully post-trained 7B dense model (the starting point) — BAR-2x7B-Base — 2-expert MoE (anchor + base pre-trained model) — BAR-2x7B-Math-SFT — math expert after mid-training and SFT — BAR-2x7B-Math — math expert after mid-training + SFT + RLVR — BAR-2x7B-Code-SFT — code expert after mid-training and SFT — BAR-2x7B-Code — code expert after mid-training + SFT + RLVR — BAR-2x7B-Tool-Use — tool use expert (SFT only) — BAR-2x7B-Safety — safety expert (SFT only) — See the paper for per-benchmark results and full evaluation details. This model is licensed…
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Автор: allenai
Теги: flex_olmo, bar, mixture-of-experts, olmo, conversational, endpoints_compatible
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Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
