BAR

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.

All BAR models are built on top of Olmo 2 7B.

Models in the BAR suite

• 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)
• BAR-5x7B — final 5-expert MoE combining all experts with a trained router

Results

Scores are unweighted averages across benchmarks within each category. See the paper for per-benchmark results and full evaluation details.

License

This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.