[
    {
        "id": "osp-17517",
        "type": "article-journal",
        "title": "DLoop: Looped Speculative Decoding",
        "author": [
            {
                "family": "Gu",
                "given": "Geonmo"
            },
            {
                "family": "Heo",
                "given": "Byeongho"
            },
            {
                "family": "Jun",
                "given": "HeeJae"
            },
            {
                "family": "Kang",
                "given": "Yoohoon"
            },
            {
                "family": "Lee",
                "given": "Sangmin"
            },
            {
                "family": "Yun",
                "given": "Sangdoo"
            },
            {
                "family": "Han",
                "given": "Dongyoon"
            }
        ],
        "URL": "https://omanscience.com/en/articles/dloop-looped-speculative-decoding",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Speculative decoding accelerates autoregressive generation in large language models. In each drafting stage, a lightweight draft model proposes tokens that the target model subsequently verifies. With increasingly capable draft models, we find that the target model frequently accepts all tokens produced in a drafting stage. A verification nevertheless follows each drafting stage, resulting in unnecessary target-model forward passes even when drafting could have continued. Adaptive draft length methods decide during decoding how many draft tokens precede a verification, but they raise the speedup only for autoregressive draft models. For a parallel draft model, drafting further requires target-model hidden states for draft tokens that have not been verified. We propose DLoop, a looped form of speculative decoding that adaptively performs multiple drafting stages before verification. DLoop continues drafting while the draft model remains confident and verifies all accumulated draft tokens together. Loop-aware training keeps the draft model reliable in the additional drafting stages by exposing it to its own hidden states for unverified draft tokens. By spending additional draft-model forward passes, DLoop reduces the number of target-model forward passes required for verification. Across diverse speculative decoding methods including EAGLE-3, DFlash, Domino, DSpark, and multi-token prediction modules, DLoop improves the wall-clock speedup by 5 to 41 percent while preserving lossless decoding. Code will be available at https://github.com/naver-ai/DLoop."
    }
]