Papers with runtime speed

2 papers
Text Compression for Efficient Language Generation (2025.naacl-srw)

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Challenge: Existing models rely on sub-word tokens for text generation, but there is no evidence for a more efficient way to generate text.
Approach: They propose a hierarchical transformer language model capable of text generation by compressing text into sentence embeddings and employing a sentence attention mechanism.
Outcome: The proposed model achieves an up to an order of magnitude improvement in FLOPs efficiency and a threefold increase in runtime speed compared to equally-sized models in the low-size regime.
SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding (2025.coling-main)

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Challenge: Large Language Models (LLMs) have remarkable emergent abilities across various tasks, yet their performance on complex reasoning and planning tasks remains suboptimal.
Approach: They propose a tree-search-based reasoning framework that encourages the exploration of intermediate steps and a round-scheduled strategy to manage draft model dispatching.
Outcome: The proposed framework improves runtime speed and GPU memory management concurrently and handles multiple iterations for thought generation and state evaluation.

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