Towards Spec Learning: Inference-Time Alignment from Preference Pairs
Steering a large language model (LLM) toward a desired behavior typically relies on an iterative process of hand-crafting a prompt based on a careful inspection of the model's responses. This is an involved, brittle, and error-prone process. Preference-based fine-tuning is a more rigorous but often prohibitively expensive solution. We propose spec learning, a framework that relies on a brief user instruction and a small set of preference judgments. These are compiled into specifications in the form of natural-language prompts for an LLM. Specifications condition LLMs at inference time, and no parameter updates to the underlying models are required. We show that the responses generated bas...
| Authors | Dhriti Krishnan, Tejas Goyal, Jaromir Savelka |
|---|---|
| Primary category | cs.CL |
| Published | 22 June 2026 |
| Last updated at source | 29 June 2026 |
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