Byte models show higher performance ceiling than token models in large-scale study

Researchers have explored the performance differences between token-based and byte-based language models, particularly in the context of distillation. They introduced two methods, Marginalize-It and End-Of-Token, to convert token logits to byte logits. Their large-scale study found that while token models initially outperform byte models at lower compute levels, byte models eventually surpass them with increased compute, reaching a higher performance ceiling. Distilled byte models also demonstrated greater data efficiency and reduced storage costs. AI

IMPACT Byte-based models may offer a more scalable and efficient path for future language model development.

RANK_REASON The cluster contains an academic paper detailing a new study on language model architectures and performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Bytes-1B
• End-Of-Token
• Gemma 2B
• Gemma-3-1B-pt
• Llama 3.2:1b
• Marginalize-It
• Token-1B

• paper
• model release

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