ConFu：为更好的推测采样展望未来
ConFu: Contemplate the Future for Better Speculative Sampling
AI总结：
ConFu提出一种新的推测解码框架，通过引入展望令牌和软提示，使草案模型能利用目标模型的未来信号，提升生成速度和接受率。
摘要：
推测解码通过轻量级草案模型生成候选令牌，再由目标模型验证，其效果依赖于草案模型的质量。尽管EAGLE系列实现了最先进的加速，但现有草案模型受限于误差累积：仅基于当前前缀生成，导致预测偏离目标模型。本文提出ConFu（Contemplate the Future），一种新的推测解码框架，使草案模型能预判生成方向。ConFu引入（i）展望令牌和软提示，允许草案模型利用目标模型的未来信号；（ii）动态展望令牌机制结合MoE，实现上下文感知的未来预测；（iii）包含锚令牌采样和未来预测复制的训练框架，学习稳健的未来预测。ConFu在Llama-3 3B/8B和Qwen-3 4B下游任务中，相比EAGLE-3在token接受率和生成速度上分别提升8-11%和约20%。我们相信，本文工作首次将推测解码与连续推理令牌结合，为加速LLM推理开辟了新方向。
英文摘要：
Speculative decoding has emerged as a powerful approach to accelerate large language model (LLM) inference by employing lightweight draft models to propose candidate tokens that are subsequently verified by the target model. The effectiveness of this paradigm critically depends on the quality of the draft model. While recent advances such as the EAGLE series achieve state-of-the-art speedup, existing draft models remain limited by error accumulation: they condition only on the current prefix, causing their predictions to drift from the target model over steps. In this work, we propose \textbf{ConFu} (Contemplate the Future), a novel speculative decoding framework that enables draft models to anticipate the future direction of generation. ConFu introduces (i) contemplate tokens and soft prompts that allow the draft model to leverage future-oriented signals from the target model at negligible cost, (ii) a dynamic contemplate token mechanism with MoE to enable context-aware future prediction, and (iii) a training framework with anchor token sampling and future prediction replication that learns robust future prediction. ConFu improves token acceptance rates and generation speed over EAGLE-3 by 8--11\% on Llama-3 3B/8B and by approximately 20\% on Qwen-3 4B across downstream tasks. We believe our work is the first to bridge speculative decoding with continuous reasoning tokens, offering a new direction for accelerating LLM inference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
