Encoder Model Evolution and Whether Decoder-only LLMs Can Replace Them: From BERT to ModernBERT

Forum topic · ✨步子哥 · 2026-06-28

Summary

This article reviews the development of encoder-only language models from BERT (2018) through its successors RoBERTa, ALBERT, ELECTRA, DeBERTa, and DeBERTa-v3, then examines the rise of decoder-only architectures such as GPT, LLaMA, Qwen, and DeepSeek. It details ModernBERT (2024), a modernized encoder family from Answer.AI and LightOn featuring 8192-token context, RoPE positional embeddings, GeGLU activations, alternating local-global attention, and 2-trillion-token training on diverse web, code, and scientific data. ModernBERT achieves roughly 4x the speed of DeBERTa with under one-fifth the memory, and tops GLUE and long-context retrieval benchmarks at base scale. The analysis concludes that encoder and decoder models are complementary rather than interchangeable: encoders excel at fast, cheap understanding tasks like classification, retrieval, and NER, while decoders lead in generation and few-shot generalization. A cited cost example shows filtering 15 trillion tokens with a fine-tuned BERT cost about $60,000 versus over $1 million with a large decoder API. Typical RAG systems combine both, with encoders retrieving and decoders generating.

Encoder Model Evolution and Whether Decoder-only LLMs Can Replace Them

This forum post reviews the development trajectory of encoder-only Transformer models (BERT through ModernBERT), the rise of decoder-only LLMs (GPT, LLaMA, Qwen, DeepSeek), and analyzes whether the two architectures can replace each other. The conclusion: they are complementary, not substitutes.

1. Encoder-only Evolution: BERT to ModernBERT

BERT (2018): The first large-scale bidirectional Transformer encoder, pretrained with masked language modeling (MLM). Breakthrough on NLU tasks (classification, NER), but cannot generate text. Despite generative AI trends, it remains the second most-downloaded model on Hugging Face, driven by retrieval and classification use cases.
Post-BERT improvements, mostly trading efficiency for accuracy:
RoBERTa (2019): removed NSP, larger batches/data, beat BERT on GLUE.
ALBERT (2019): parameter sharing and embedding factorization for lightweight models; SOP replaces NSP.
ELECTRA (2020): generator-discriminator pretraining via replaced token detection (RTD).
DeBERTa (2021) / DeBERTa-v3: disentangled attention, relative positions; top accuracy but larger and slower.
Others: SpanBERT, XLNet, ERNIE — task-specific gains with trade-offs.

ModernBERT (2024)

Released by Answer.AI and LightOn, ModernBERT is a modernized encoder achieving a "Pareto improvement" — better on speed, accuracy, and context length simultaneously:

8192-token native context (16x the typical 512).
~4x faster than DeBERTa on variable-length input, ~3x faster than other high-quality models at long context; less than 1/5 the memory.
First Base-scale model to beat DeBERTa-v3 on GLUE; SOTA on long-context retrieval (ColBERT, +9 points); strong code retrieval thanks to code-heavy training data.
Architecture upgrades: RoPE positional embeddings, GeGLU MLPs, removed biases, extra normalization, and alternating attention (local sliding window of 128 tokens with global attention every 3 layers).
Training data: 2 trillion tokens of web documents, code, and scientific articles, mostly unique (no 20-40x repetition).

2. Decoder-only Models: Strengths and Limits

Represented by GPT, LLaMA, Qwen, DeepSeek, decoder-only models are autoregressive and dominant in modern LLMs.

Strengths:

General-purpose generation (dialogue, code, summarization) with strong zero-shot/few-shot in-context learning.
Higher performance ceiling at scale; efficient parallel training via causal masking and KV-Cache inference.

Limits:

Huge parameter counts (e.g., LLaMA-3.1 at 405B) make them "too big, too slow, too expensive" for many workloads; hard to deploy locally for privacy-sensitive uses.
Unidirectional attention limits global context use at understanding time.
For narrow tasks (classification, retrieval), fine-tuned encoder models are often more economical with comparable results.

3. Can They Replace Each Other?

Task fit: encoders produce bidirectional, context-aware representations — ideal for classification, retrieval, NER; decoders for generation and cross-task generalization.
Cost example (FineWeb-Edu): filtering 15 trillion tokens with a large decoder (Gemini Flash API) would exceed $1 million; using a fine-tuned BERT on 6000 H100s for 6000 hours cost about $60,000.
Synergy: in RAG systems, an encoder retrieves passages and a decoder generates the answer. Some research explores unified architectures blending bidirectional and causal attention.

Conclusion

ModernBERT proves encoder-only architectures are far from obsolete. Encoder and decoder models serve different needs — fast, cheap understanding vs. flexible generation — and future systems will most likely use both cooperatively rather than treating decoder-only LLMs as universal replacements.