Use Cases

Real-world applications

Code Generation

Training models on large code corpora to improve generation accuracy and speed.

Result: Higher accuracy (95%) on code tasks compared to single-token prediction baselines.

Efficient Large Language Model Training

Incorporating MTP into training pipelines to densify training signals and improve data efficiency.

Result: Improved benchmark performance and training efficiency.

Faster Inference via Speculative Decoding

Using MTP-enabled models like GLM-4.5 to perform speculative decoding during inference.

Result: Reduced inference latency.