@galoisextn: Holy shit there’s only bots out here spreading slop, not one comment about the graphic
Summary
A paper from Meta shows that byte-level models start behind token models but surpass them with increasing compute, demonstrated with distilled 1B models trained on up to 1 trillion bytes.
View Cached Full Text
Cached at: 09/14/26, 05:30 PM
Holy shit there’s only bots out here spreading slop, not one comment about the graphic
elvis (@omarsar0): Banger paper from Meta.
This work shows that byte-level models start out behind token models and then pass them as compute grows.
They show this for distilled 1B models trained on up to 1 trillion bytes.
To distill a byte student from a token teacher, they convert the
Similar Articles
@MindsAI_Jack: Step in the right direction. As far as I know, pioneered at Google originally with ByT5 that was more resilient against…
A research paper from Meta demonstrates that byte-level language models, initially inferior to token-based models, can outperform them as computational resources increase, shown through distilled 1B models trained on up to 1 trillion bytes.
@Flopsie4: Hopefully in future work it's also explore how these byte models respond to quantization. If they respond the same or e…
A discussion on future work exploring how byte-level models respond to quantization for potential improvements in local AI, based on a Meta paper showing byte models outperforming token models as compute scales.
@gnjrdi: High levels of bullshitery
A tweet claims that with only 5,000 training examples, 4 hours of training, and $80, a 4B parameter model can beat a frontier model like GPT-5.6, arguing that training data is now cheap and plentiful thanks to capable coding agents.
Breaking the Token Ceiling: Distilling Smaller, Stronger Byte Models
This paper presents a large-scale study comparing distilled byte and token models, finding that byte models achieve higher performance ceilings with more compute and are more data-efficient than token models.
@0x0SojalSec: Final take : Tencent recently drop a 295B parameter model that only activates 21B params per token. While most labs are…
Tencent released Hy3, a 295B parameter MoE model with 21B active parameters per token, competitive with larger models on agentic coding and tool use tasks, with Apache 2.0 weights.