[Submitted on 10 Oct 2026]
Title:Can Language Models Learn While They Think? Testing Gradient Adaptation Across Architectures
View a PDF of the paper titled Can Language Models Learn While They Think? Testing Gradient Adaptation Across Architectures, by Surya Saka
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Abstract:Letting a language model take gradient steps on the text it is reading, known as dynamic evaluation, lowers its loss on the rest of the document. Its behaviour on hybrid linear-attention architectures, on diffusion language models, and on standard long-context benchmarks has not been measured. We measure it. With one SGD step per 128-token chunk on the MLP output projections of the last quarter of blocks, no training, and a 2k-token window, softmax-attention models (Qwen3-1.7B, Qwen3-4B, Gemma-4-E4B) lose 5-8% of their next-token loss over a document and outperform the same frozen model given the full 32k-token context. Hybrid Gated-DeltaNet models (Qwen3.5-4B, Qwen3.8-27B) gain under 1% at every learning rate: the linear-attention blocks already perform the adaptation. The mechanism transfers to masked-diffusion models with a larger effect, 8% on Nemotron-Diffusion-8B and 17-20% on DiffusionGemma-26B, once the rate is lowered and updates are clipped. Across RULER, BABILong, LongBench-v2, SCOTUS, HotpotQA, MuSiQue, ContractNLI and citation completion, at 100 or more items on two model sizes, weights updated on the full document recover none of the accuracy lost to context truncation: the adaptation is style and vocabulary, not retrieval. Writing facts with the next-token loss stores the prompt format rather than the fact (30% recall under chat prompting against 80% in context) and costs 36-38 GSM8K points at every dose. Writing a conclusion as behaviour instead, by distilling the model's own conclusion-conditioned judgments into a low-rank adapter, reaches the in-context ceiling (0.83 and 0.88 against 0.84 and 0.88 on 100 games) with GSM8K unchanged, and the conclusion can come from an inquiry loop that found it by asking information-gain questions.
Comments: 19 pages, 13 figures, 12 tables
Subjects:
Machine Learning (cs.LG); Computation and Language (cs.CL)
MSC classes: 68T50, 68T07
ACM classes: I.2.7; I.2.6
Cite as: arXiv:2610.33025 [cs.LG]
(or arXiv:2610.33025v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.33025
arXiv-issued DOI via DataCite
Submission history
From: Surya Saka [view email] [v1] Sat, 10 Oct 2026 17:32:08 UTC (25 KB)
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