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Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

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arxiv 2403.07816 v1 pith:7QLHQ4WX submitted 2024-03-12 cs.CL cs.AI

Sainbayar Sukhbaatar , Olga Golovneva , Vasu Sharma , Hu Xu , Xi Victoria Lin , Baptiste Rozière , Jacob Kahn , Daniel Li
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Wen-tau Yih Jason Weston Xian Li
This is my paper
classification cs.CLcs.AI
keywords expertsstageasynchronouslybranch-train-mixlearnllmsmethodparameters
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We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge. Our method, named Branch-Train-MiX (BTX), starts from a seed model, which is branched to train experts in embarrassingly parallel fashion with high throughput and reduced communication cost. After individual experts are asynchronously trained, BTX brings together their feedforward parameters as experts in Mixture-of-Expert (MoE) layers and averages the remaining parameters, followed by an MoE-finetuning stage to learn token-level routing. BTX generalizes two special cases, the Branch-Train-Merge method, which does not have the MoE finetuning stage to learn routing, and sparse upcycling, which omits the stage of training experts asynchronously. Compared to alternative approaches, BTX achieves the best accuracy-efficiency tradeoff.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FlexOlmo: Open Language Models for Flexible Data Use

    cs.CL 2025-07 conditional novelty 7.0 of 10

    FlexOlmo merges independently trained language-model experts, trained on private data, into a single mixture-of-experts model without joint training.

  2. Decentralised AI Training and Inference with BlockTrain

    cs.AI 2026-06 unverdicted novelty 6.0 of 10

    BlockTrain partitions models into blocks trained on local objectives, reaching CE 1.359 on WikiText within 0.04 of end-to-end baseline while enabling distributed training and inference over TCP for up to 75B-parameter models.

  3. Local Mixtures of Experts: Essentially Free Test-Time Training via Model Merging

    cs.LG 2025-05 conditional novelty 6.0 of 10

    TTMM pre-trains many local LoRA experts on data clusters and merges the most relevant few at test time, approximating test-time training with a 100x speedup and near-TTT perplexity.

  4. Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts

    cs.CL 2025-07 conditional novelty 5.0 of 10

    Nile-Chat models for dual-script Egyptian Arabic beat strong baselines on newly translated benchmarks, but the evaluation may be inflated by training/eval data overlap and Claude-generated script data.

  5. Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs

    cs.CL 2025-06 reject novelty 5.0 of 10

    ASTS extends locally typical sampling with semantic scoring and dynamic thresholds, reporting improved perplexity, MAUVE, and diversity on story and summarization tasks.

  6. Enhancing Multimodal Continual Instruction Tuning with BranchLoRA

    cs.CL 2025-05 conditional novelty 5.0 of 10

    BranchLoRA reduces catastrophic forgetting in multimodal continual instruction tuning by using a shared LoRA matrix, task-specific branches, frozen experts, and learned task keys.

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