Towards Spec Learning: Inference-Time Alignment from Preference Pairs
Quick Answer
The proposed 'spec learning' framework enables large language models to align with user preferences using brief instructions and preference judgments, outperforming direct preference optimization in specialized domains without requiring parameter updates.
Quick Take
This method enhances interpretability and transparency of model responses.
Key Points
- Spec learning compiles user instructions into natural-language prompts for .
- No parameter updates are needed, making it less brittle than traditional methods.
- Outperforms on dense preference signal datasets.
- Specifications are human-readable, enhancing interpretability and transparency.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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 based on the compiled specifications often outperform direct preference optimization (DPO) on datasets from specialized domains whose preference signal is dense. Unlike opaque weight updates, the resulting specifications are human-readable and double as interpretable and transparent written embodiments of the preference signal that produced them.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.24004 [cs.CL] |
| (or arXiv:2606.24004v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24004 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tejas Goyal [view email]
[v1]
Mon, 22 Jun 2026 23:21:55 UTC (114 KB)
— Originally published at arxiv.org
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