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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