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DFlash
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dflash-fd015eda·5 events·first seen Aliases: DFlash
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AdaFlash: Adaptive speculative decoding with on-policy distilled diffusion drafters
AdaFlash is a new framework for accelerating LLM inference via speculative decoding that addresses variance problems in diffusion-based draft models. It combines an on-policy distillation algorithm using reverse-KL divergence with an adaptive length head that dynamically adjusts candidate sequence length. Experiments show up to ~66% higher throughput than prior state-of-the-art methods, with especially large gains in high-concurrency deployment scenarios.
DeepSeek open-sources DSpark speculative decoding module, achieving 50%+ throughput gains in production
Researchers from Peking University and DeepSeek introduced DSpark, a speculative decoding module that dynamically adjusts verification depth based on server load, achieving 57–85% faster per-user token generation and 51–52% higher total throughput compared to DeepSeek's previous production drafter. The team released checkpoints DeepSeek-V4-Pro-DSpark and DeepSeek-V4-Flash-DSpark on Hugging Face under an MIT license, with the draft module attaching to frozen target model weights. Key innovations include a parallel drafting backbone (adapted from DFlash), a Markov head for sequential token coherence correction, a calibrated confidence head, and a load-aware scheduler that trades draft length against server capacity. Results generalize across model families including Qwen3 and Gemma4.
DominoTree: Training-free conditional tree drafting for speculative decoding achieves 6.6x LLM inference speedup
DominoTree is a new training-free speculative decoding method that constructs best-first draft trees scored by Domino's conditional, non-factorized GRU-based correction along each root-to-node path. On Qwen3-4B across eight benchmarks, it achieves up to 6.6x speedup over autoregressive decoding and a mean accept length of up to 10.7 tokens per round, outperforming prior methods including DDTree, CaDDTree, DFlash, and the base Domino decoder. A GPU-native CUDA-graph tree builder provides 9-10% throughput gains over Domino overall, with up to +22% on Alpaca, while maintaining bit-identical acceptance behavior.
DeLS-Spec: Decoupled long-short context speculative decoding improves drafting efficiency
DeLS-Spec is a new speculative decoding method that combines a fixed block-parallel draft model (DFlash) as a long-context expert with a lightweight locally-trained short-context head, avoiding joint training with the target model. The approach introduces intra-block causal conditioning at low training cost and is modular across DFlash checkpoints. Experiments on Qwen3 models show consistent speedup and acceptance-length improvements over DFlash on math, code, and dialogue benchmarks.
Graft: Hybrid Tree Construction for Speculative Decoding via Prune-Then-Retrieve
Graft is a training-free framework that improves speculative decoding by coupling dynamic-depth pruning with retrieval-based token compensation. Pruning reduces VRAM and compute overhead while freeing budget for retrieval, which fills topological gaps in the draft tree with near-zero additional cost. On short-context benchmarks, Graft achieves up to 5.41× speedup and improves average speedup over EAGLE-3 by up to 21.8% on Qwen3-235B. The method is evaluated across short- and long-context settings and extended to block-drafting paradigms.