Listen to this Post
Introduction: A Shift in Language Model Pretraining
In the world of Natural Language Processing (NLP), the way we pretrain language models significantly impacts their ability to perform downstream tasks like text classification, question answering, and information retrieval. Traditionally, encoder models like BERT rely on Masked Language Modeling (MLM), where words are hidden during training and predicted by the model. However, a recent wave of research challenges this norm, pointing out that Causal Language Modeling (CLM)—more commonly used with decoder-based models like GPT—might offer substantial benefits for encoders too.
This article explores groundbreaking research that shows hybrid pretraining approaches and adaptation strategies can outperform traditional MLM-based training. With over 30 models trained, 15,000+ evaluations, and 110k GPU hours, the study delivers compelling evidence that the future of encoders may lie in combining MLM and CLM, rather than relying solely on one.
Hybrid Pretraining Outperforms MLM Alone
The original research begins by addressing a fundamental question in model pretraining: Are the advantages of CLM-based models like GPT simply due to their scale, or does the CLM objective itself offer unique benefits? To answer this, the researchers ran extensive experiments across consistent model sizes and compute budgets.
They designed two major setups:
- Pretraining from scratch using different CLM-MLM objective splits.
- Continued Pretraining (CPT), adapting existing models with further training.
Masked vs. Causal: The Hybrid Approach
To assess the power of hybrid pretraining, they tested five configurations:
100% MLM
75% MLM / 25% CLM
50% MLM / 50% CLM
25% MLM / 75% CLM
100% CLM
These models were evaluated on a wide array of downstream tasks including text classification (TC), sentence classification (SC), question answering (QA), and information retrieval (IR).
Findings:
Hybrid models consistently outperformed pure MLM models.
Performance gains varied across tasks and training steps.
The 50-50 split often provided the best trade-off between compute efficiency and performance.
Continued Pretraining for Better Encoders
The research further investigated whether continued training—starting from CLM and adapting with MLM—yielded better results than continued MLM-only training. Using a mid-sized model (610M parameters) and a consistent 22k-step compute budget:
CLM models adapted with MLM outperformed MLM-only models across almost all tasks.
On sentence classification, the performance gap widened in favor of the CLM-then-MLM strategy.
For QA and IR, the adapted CLM models closed the gap entirely with or surpassed MLM baselines.
These results are a clear indication that starting with a decoder (CLM) and transitioning to encoder-style training (MLM) can produce more efficient and high-performing models.
Real-World Impact and Future Applications
This approach could prove revolutionary in building resource-efficient, high-performing language models, especially for low-resource scenarios or industry applications where compute budgets are tight. Furthermore, the authors suggest that their findings could enhance Vision-Language Models (VLMs), which often use decoder-style architectures and could benefit from hybrid pretraining strategies.
All models, code, and data have been made open-source to promote further innovation and reproducibility in this growing field.
What Undercode Say: 🔍
Unpacking the Implications of CLM + MLM Synergy
At Undercode, we view this research as a significant pivot point in the field of encoder pretraining. Traditionally, MLM has dominated the scene, with BERT-like models setting benchmarks across nearly all tasks. However, this study brings CLM-based strategies into the spotlight, proving that they are not just decoder-specific tools, but can be instrumental in training stronger encoders too.
Analysis of Training Efficiency
By using controlled setups—same model size, data, and compute—the research removes most confounding variables. This makes their findings particularly compelling. What stands out most is the efficiency of hybrid models, especially under tight compute budgets. Models trained with a 50-50 CLM/MLM objective reach or exceed performance benchmarks of models trained entirely with MLM, while using fewer resources in some cases.
Decoder-Encoder Transition: A Game-Changer
The study’s continued pretraining setup shows that starting with a CLM-pretrained model and switching to MLM yields stronger final models. This is an inversion of the traditional encoder-first approach and highlights the untapped potential of decoder architectures when used creatively.
Potential for Domain-Specific Models
Hybrid training also opens up new possibilities in domain-specific modeling. For instance, in finance or legal NLP where labeled data is scarce, starting with a general-purpose CLM model and adapting it via MLM could yield robust, task-specific models faster and cheaper than current methods.
Application to Vision-Language Models (VLMs)
The final point about VLMs is particularly intriguing. If CLM pretraining boosts encoder performance in text-only settings, it may offer similar gains in multimodal settings, where many models (like Flamingo, BLIP) rely on decoder-heavy backbones. This could catalyze a new era of cross-modal representation learning.
✅ Fact Checker Results
✅ True: Hybrid objectives (CLM + MLM) outperform MLM-only models in most NLP downstream tasks.
✅ True: Continued pretraining using MLM on CLM-initialized models boosts performance, especially in sentence classification.
❌ False: MLM is the only viable objective for pretraining encoders—CLM has now proven effective when used wisely.
🔮 Prediction: The Rise of CLM-Augmented Encoders
Given the empirical success and efficiency of hybrid pretraining, it’s likely that future encoder architectures will no longer be trained with MLM alone. We predict that:
MLM-only pretraining will decline as hybrid strategies prove more effective.
Toolkits and libraries will start offering CLM-to-MLM pipelines as standard templates.
Multimodal models will adopt similar training strategies to bridge the encoder-decoder divide.
The line between “encoder” and “decoder” will blur, paving the way for unified language representation models optimized for a variety of downstream tasks.
References:
Reported By: huggingface.co
Extra Source Hub:
https://www.medium.com
Wikipedia
OpenAi & Undercode AI
Image Source:
Unsplash
Undercode AI DI v2
