HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning

HyperThink is a text-to-parameter hypernetwork that replaces long-form reasoning traces in large language models with a single, query-conditioned parameter update. The hypernetwork predicts updates to a small subset of the base model’s parameters, and a vector-quantized decoder limits these updates to reusable patterns, improving robustness and transfer. Trained end-to-end on the base model’s outputs, HyperThink eliminates the need for intermediate reasoning traces at test time, producing concise step-by-step solutions with fewer tokens while maintaining strong reasoning performance and improving the accuracy‑latency trade‑off on mathematical and general reasoning tasks.

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• llms
• efficiency