HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning
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Computer Science > Computation and Language
arXiv:2610.03039 (cs)
[Submitted on 2 Oct 2026]
Title:HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning
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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.
| Comments: | COLM 2026 |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.03039 [cs.CL] |
| (or arXiv:2610.03039v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03039
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning, by Donggyun Kim and 4 other authors
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