Draft-OPD introduces an on‑policy distillation method for speculative draft models, addressing the mismatch between supervised fine‑tuning and inference by letting the target model supervise the drafter on draft‑induced states. The approach uses target‑assisted rollouts for stable continuations and replays drafting from error positions exposed during verification, enabling the drafter to learn from both accepted and rejected proposals. Experiments demonstrate that Draft‑OPD achieves more than five‑fold lossless acceleration across diverse tasks, outperforming prior draft models such as EAGLE‑3 and DFlash by 23 % and 13 % respectively.
By Haodi Lei, Yafu Li, Haoran Zhang, Shunkai Zhang, Qianjia Cheng, Xiaoye Qu, Ganqu Cui, Bowen Zhou, Ning Ding, Yun Luo, Yu Cheng
LEAP: Learning Efficient Action Proposals For LLM Agents proposes a method to speed up large language model agents by training a small 0.6B drafter to predict target actions accurately. The approach uses a latency framework that balances drafting, verification, and execution costs, achieving up to 60% faster end‑to‑end wall clock time without reducing task success. LEAP can be online trained, eliminating the need for prior trace collection and making it practical for real‑world deployment.
By Zhen Xu, Qizheng Zhang, Gerry Wan, Shang Zhu, Ce Zhang
The paper introduces Trajectory-Local Adaptive Retrieval (TLAR), a method that leverages a model’s own generated history to improve speculative decoding. TLAR retrieves continuations that closely match the current trajectory and adapts retrieval activation and candidate width based on recent verification outcomes, integrating retrieved and model-generated drafts into a shared candidate tree. Experiments on code debugging, mathematics, and open-ended writing show that TLAR enhances token acceptance under matched verification budgets and boosts overall throughput compared to a draft-model baseline.
By Yuyang Dai, Yushun Dong
arXiv:2609. 05019v1 Announce Type: new Abstract: Agents tend to optimize, select, or constrain execution structures before decisive runtime outcomes are observed.
By Tianxing Wang, Mingming Zhao, Shuai Huang, Huiyang Xu, Chaoyue Niu, Shengzhong Liu, Fan Wu
The paper introduces the Agent-Editing World Model (AEWM), a new approach that models how reasoning and actions influence future task progress instead of simulating tool responses. AEWM includes an Action Judge that classifies decisions as Critical, Exploratory, or Noisy, and a State Revision mechanism that edits noisy reasoning–action continuations from the same observed history. The integrated system, EditAct, directly updates the underlying state during real execution, leading to significant performance gains across multiple benchmarks and agent backbones.
By Shuang Sun, Guoxin Chen, Fanzhe Meng, Jia Deng, Huatong Song, Jinhao Jiang, Wayne Xin Zhao, Hongteng Xu, Ji-Rong Wen
The paper introduces AdaptiveSpec, a training‑free speculative decoding method that simultaneously adapts the per‑step verification rule and the draft‑tree shape using signals generated during decoding. It replaces the fixed token‑match rule with a margin‑based threshold and adjusts tree depth, width, and node count based on draft confidence and recent acceptance history, allowing the total draft count to vary. Experiments on SGLang show up to 56% throughput gains over EAGLE‑3 while maintaining 93% of lossless task accuracy on GSM8K, MATH‑500, and HumanEval across three models.
By Oszk\'ar Urb\'an, Young D. Kwon, Stylianos I. Venieris, Cecilia Mascolo