domino-9a6d337b·3 events·first seen Aliases: DOMINO
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 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.
Alibaba's Qwen team presents Qwen-VLA, a unified embodied foundation model that extends the Qwen vision-language stack to continuous action and trajectory generation via a DiT-based action decoder. The model is jointly pretrained on diverse data spanning manipulation trajectories, egocentric demonstrations, synthetic simulation, and navigation data, with embodiment-aware prompt conditioning to support multiple robot platforms. A unified action-and-trajectory prediction framework covers manipulation, navigation, and trajectory prediction tasks. Benchmarks show strong results: 97.9% on LIBERO, 73.7% on Simpler-WidowX, 69.0% OSR on R2R navigation, and 76.9% average OOD success in real-world ALOHA experiments.