CAST: Cost-Aware Speculative Trees from One-Pass Block Drafters
Speculative decoding accelerates large language model inference by drafting future tokens cheaply and verifying them with the target model in parallel. Block drafters score a whole block of future tokens in one forward pass, yet standard decoding verifies only the top-scoring chain and discards the other candidates. Because these candidates are already scored, verifying more of them adds target computation but no extra drafting. We introduce CAST (Cost-Aware Speculative Trees), which packs these candidates into a tree and verifies it in a single target pass, leaving the target model, drafter weights, and decoding rule untouched. To decide how wide the tree should be, CAST adds candidates ...
| Authors | Jungseob Lee, Sugyeong Eo |
|---|---|
| Primary category | cs.CL |
| Published | 29 September 2026 |
| Last updated at source | 10 October 2026 |
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