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arXiv · 2610.03039

HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning

Donggyun Kim·Jack Lu·Chanwoo Kim·Mengye Ren·Seunghoon Hong
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Abstract

Long-form thinking traces can substantially improve the multi-step reasoning performance of large language models (LLMs), but they introduce high inference-time overhead, with latency dominated by sequential decoding. We propose HyperThink, a text-to-parameter approach that amortizes this reasoning computation into a single query-conditioned parameter update: a lightweight hypernetwork reads the question and predicts updates to a small subset of the base LLM's parameters, while a vector-quantized decoder constrains them to a finite set of reusable patterns to improve robustness and transfer. Trained end-to-end on outputs from the base model itself, HyperThink eliminates long thinking traces at test time: after one hypernetwork forward pass, the adapted model generates a concise step-by-step solution and final answer without an intermediate trace, using far fewer tokens while retaining strong reasoning performance. Empirically, HyperThink improves the low-latency region of the accuracy-latency trade-off on mathematical and general reasoning tasks, with its strongest gains in the near-non-thinking regime.

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Donggyun Kim, Jack Lu, Chanwoo Kim, Mengye Ren, Seunghoon Hong. 2026-10-02. HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning. https://arxiv.org/abs/2610.03039

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