arXiv cs.CL· Yue Wu, Yang Zhang, Weinan Gan, Chongming Gao, Xinsen Guo, Xiaoyuan Li, Fuli Feng·· 13 小时前AI 评分34
Select-TTT：面向 In-Place TTT 的查询条件选择性快速权重更新
Select-TTT: Query-Conditioned Selective Fast Weight Updates for In-Place Test-Time Training
AI 导读
Select-TTT 提出一种模型无关、以用户查询为条件的 In-Place TTT 写入规则，按每个 chunk 与查询的相关性缩放其写入强度，从而缓解无关内容稀释快速权重记忆的问题。
正文 · 原文
Abstract:Test-time training (TTT) enables a deployed language model to internalize incoming context on-the-fly into a small set of fast-weight parameters, rather than merely treating it as transient context for attention. In-Place TTT recently made this idea practical for large language models by repurposing the down-projection of standard MLP blocks as fast weights. However, we find that its benefit largely disappears on realistic web long-context question answering. We attribute this to In-Place TTT's uniform write schedule: every chunk is written with equal strength, so irrelevant content dilutes query-relevant information in fast-weight memory. Building on this insight, we propose Select-TTT, a model-agnostic, query-conditioned rule for In-Place TTT that scales each chunk's write strength by its relevance to the user query. This relevance signal requires no supervision and no new parameters: it is read directly from the model's frozen output head, as the probability mass that a chunk's hidden states assign to the query tokens. A temperature-scaled softmax turns these scores into learning-rate multipliers, leaving the chunk-wise, context-parallel computation of In-Place TTT intact. Select-TTT consistently improves long-context question answering on two benchmarks and two backbones. These findings highlight the value of query-conditioned fast weight writes for practical test-time training on long-context question answering, which is also potentially promising for long-horizon agent tasks involving multiple user queries over evolving contexts.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.36794 [cs.LG] |
| (or arXiv:2610.36794v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36794 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yue Wu [view email]
[v1]
Tue, 27 Oct 2026 07:58:39 UTC (249 KB)
来源：arXiv cs.CL · arxiv.org