The development of large-scale language models (LLMs) has historically required centralized access to extensive datasets, many of which are sensitive, copyrighted, or governed by usage restrictions. This constraint severely limits the participation of data-rich organizations operating in regulated or proprietary environments. FlexOlmo—introduced by researchers at the Allen Institute for AI and collaborators—proposes a modular training and inference framework that enables LLM development under data governance constraints.
Current LLMs…..
Current LLM training pipelines rely on aggregating all training data into a single corpus, which imposes a static inclusion decision and eliminates the possibility of opt-out post-training. This approach is incompatible with:
- Regulatory regimes (e.g., HIPAA, GDPR, data sovereignty laws),
- License-bound datasets (e.g., non-commercial or attribution-restricted),
- Context-sensitive data (e.g., internal source code, clinical records).
FlexOlmo addresses two objectives:
- Decentralized, modular training: Allow independently trained modules on disjoint, locally held datasets.
- Inference-time flexibility: Enable deterministic opt-in/opt-out mechanisms for dataset contributions without retraining.
Model Architecture: Expert Modularity via Mixture-of-Experts (MoE)
FlexOlmo builds upon a Mixture-of-Experts (MoE) architecture where each expert corresponds to a feedforward network (FFN) module trained independently. A fixed public model (denoted as M<sub>pub</sub>) serves as the shared anchor. Each data owner trains an expert M<sub>i</sub> using their private dataset D<sub>i</sub>, while all attention layers and other non-expert parameters remain frozen.
Key architectural components:
- Sparse activation: Only a subset of expert modules is activated per input token.
- Expert routing: Token-to-expert assignment is governed by a router matrix derived from domain-informed embeddings, eliminating the need for joint training.
- Bias regularization: A negative bias term is introduced to calibrate selection across independently trained experts, preventing over-selection of any single expert.
This design maintains interoperability among modules while enabling selective inclusion during inference.
Asynchronous and Isolated Optimization
Each expert M<sub>i</sub> is trained via a constrained procedure to ensure alignment with M<sub>pub</sub>. Specifically:
- Training is performed on a hybrid MoE instance comprising M<sub>i</sub> and M<sub>pub</sub>.
- The M<sub>pub</sub> expert and shared attention layers are frozen.
- Only the FFNs corresponding to M<sub>i</sub> and the router embeddings r<sub>i</sub> are updated.
To initialize r<sub>i</sub>, a set of samples from D<sub>i</sub> is embedded using a pretrained encoder, and their average forms the router embedding. Optional lightweight router tuning can further improve performance using proxy data from the public corpus.
Dataset Construction: FLEXMIX
The training corpus, FLEXMIX, is divided into:
- A public mix, composed of general-purpose web data.
- Seven closed sets simulating non-shareable domains: News, Reddit, Code, Academic Text, Educational Text, Creative Writing, and Math.
Each expert is trained on a disjoint subset, with no joint data access. This setup approximates real-world usage where organizations cannot pool data due to legal, ethical, or operational constraints.
Evaluation and Baseline Comparisons
FlexOlmo was evaluated on 31 benchmark tasks across 10 categories, including general language understanding (e.g., MMLU, AGIEval), generative QA (e.g., GEN5), code generation (e.g., Code4), and mathematical reasoning (e.g., Math2).
Baseline methods include:
- Model soup: Averaging weights of individually fine-tuned models.
- Branch-Train-Merge (BTM): Weighted ensembling of output probabilities.
- BTX: Converting independently trained dense models into a MoE via parameter transplant.
- Prompt-based routing: Using instruction-tuned classifiers to route queries to experts.
Compared to these methods, FlexOlmo achieves:
- A 41% average relative improvement over the base public model.
- A 10.1% improvement over the strongest merging baseline (BTM).
The gains are especially notable on tasks aligned with closed domains, confirming the utility of specialized experts.
Architectural Analysis
Several controlled experiments reveal the contribution of architectural decisions:
- Removing expert-public coordination during training significantly degrades performance.
- Randomly initialized router embeddings reduce inter-expert separability.
- Disabling the bias term skews expert selection, particularly when merging more than two experts.
Token-level routing patterns show expert specialization at specific layers. For instance, mathematical input activates the math expert at deeper layers, while introductory tokens rely on the public model. This behavior underlines the model’s expressivity compared to single-expert routing strategies.
Opt-Out and Data Governance
A key feature of FlexOlmo is deterministic opt-out capability. Removing an expert from the router matrix fully removes its influence at inference time. Experiments show that removing the News expert reduces performance on NewsG but leaves other tasks unaffected, confirming the localized influence of each expert.
Privacy Considerations
Training data extraction risks were evaluated using known attack methods. Results indicate:
- 0.1% extraction rate for a public-only model.
- 1.6% for a dense model trained on the math dataset.
- 0.7% for FlexOlmo with the math expert included.
While these rates are low, differential privacy (DP) training can be applied independently to each expert for stronger guarantees. The architecture does not preclude the use of DP or encrypted training methods.
Scalability
The FlexOlmo methodology was applied to an existing strong baseline (OLMo-2 7B), pretrained on 4T tokens. Incorporating two additional experts (Math, Code) improved average benchmark performance from 49.8 to 52.8, without retraining the core model. This demonstrates scalability and compatibility with existing training pipelines.
Conclusion
FlexOlmo introduces a principled framework for building modular LLMs under data governance constraints. Its design supports distributed training on locally maintained datasets and enables inference-time inclusion/exclusion of dataset influence. Empirical results confirm its competitiveness against both monolithic and ensemble-based baselines.
The architecture is particularly applicable to environments with:
- Data locality requirements,
- Dynamic data use policies,
- Regulatory compliance constraints.
FlexOlmo provides a viable pathway for constructing performant language models while adhering to real-world data access boundaries.
Check out the Paper, Model on Hugging Face and Codes. All credit for this research goes to the researchers of this project.
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