| title | HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning |
|---|---|
| authors | Donggyun Kim, Jack Lu, Chanwoo Kim, Mengye Ren, Seunghoon Hong |
| date | 2026-10-02 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.03039 |
| upvotes | 7 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| 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. |
cite
@misc{kim2026hyperthink,
title={HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning},
author={Donggyun Kim and Jack Lu and Chanwoo Kim and Mengye Ren and Seunghoon Hong},
year={2026},
eprint={2610.03039},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.03039}
}