HullFT 解决了测试时微调中检索和微调的双重瓶颈，做 LLM 推理优化的开发者可以直接尝试，能显著提升效率。
测试时微调（TTFT）是一种新兴范式，通过检索相关序列并更新模型来适应每个提示，但现有方法在速度和效果间存在权衡。HullFT 提出几何方法，利用 Frank-Wolfe 优化将查询嵌入表示为稀疏凸组合，生成相关且多样化的支持集。通过几何整数化过程将分数权重转换为精确整数多重集，并利用梯度重用技术摊销重复微调的计算成本。实验表明，HullFT 在更低总运行时间下实现了更低的 bits-per-byte，优于当前最先进的 TTFT 方法。
Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching
Test-time finetuning (TTFT) is a rapidly evolving paradigm that adapts a language model to each prompt by retrieving related sequences, updating the model on them, and then evaluating the prompt. However, TTFT is only practical if it is fast: selection and finetuning both happen per query, making each a direct bottleneck. Existing methods trade speed for quality: fast retrieval is often redundant, while stronger diversity-aware selection adds prohibitive per-query cost. We introduce HullFT, a geometric approach to TTFT that addresses both bottlenecks. Given a query, HullFT first represents the query embedding as a sparse convex combination of few training sequences, using efficient projection-free Frank-Wolfe optimization. This yields a support set that is inherently relevant and diverse. We then convert the fractional convex weights into an exact integer multiset for finetuning through a geometric integerization procedure. The resulting multiplicities naturally create repeated examples, which we exploit with Gradient Reuse to amortize forward-backward computation across repeated finetuning steps. Our experiments show that HullFT improves the quality-efficiency tradeoff over current state-of-the-art TTFT methods, achieving lower bits-per-byte at substantially lower total runtime.