# FLEET：从 Logits 熵到增强的文本生成轨迹

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-23 08:00
- AIHOT 分数：36
- AIHOT 链接：https://aihot.news/items/cmufdsjtd07jvroagf60az9a8
- 原文链接：https://arxiv.org/abs/2609.27657

## AI 摘要

FLEET 将每次生成表示为熵超过阈值的稀疏轨迹，并据此推断逐 token 效用分数来调整 logits，从而在生成过程中引入记忆机制。基准评测显示，FLEET 在达到与重复采样基线相同准确率的同时实现 3x 加速，并在相同预算下将 LiveCodeBench Pass@32 从 59.9% 提升至 66.2%。

## 正文

Solutions based on large language models (LLMs) often rely on temperature sampling to improve accuracy and stability by aggregating multiple samples from the completion distribution. However, this memoryless approach is inherently suboptimal: because it lacks awareness of prior generations and their evaluations, it produces an increasing proportion of semantically duplicate answers as more samples are drawn, leading to diminishing returns. To address this limitation, we introduce FLEET, a novel method that integrates a memory mechanism into the generation process.

FLEET represents each generation as a sparse trajectory through states whose entropy exceeds a predefined threshold and uses these trajectories to infer per-token utility scores that adjust the logits. Benchmark evaluations demonstrate that FLEET achieves the same accuracy as the repeated sampling baseline, with a 3x speedup, and substantially improves accuracy on complex coding tasks (LiveCodeBench Pass@32 increases from 59.9% to 66.2%) under the same budget. Furthermore, in the greedy-decoding configuration evaluated here, the approach is deterministic and uses a single calibration pass to derive its principal hyperparameters, requiring only minimal modifications to existing LLM pipelines.