Collective alignment: public input on our Model Spec

Collective alignment: public input on our Model Spec | OpenAI

August 27, 2025

Collective alignment: public input on our Model Spec

We surveyed over 1,000 people worldwide on how our models should behave and compared their views to our Model Spec. We found they largely agree with the Spec, and we adopted changes from the disagreements.

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No single person or institution should define how an ideal AI should behave for everyone.

To fulfill our mission of ensuring that AGI benefits all of humanity, OpenAI needs to build systems that reflect the wide range of values and priorities of all the people we serve. We approach this in many ways, including external feedback forms, expert input, and global listening sessions. Another way we do this is through collective alignment, a research effort that gathers a variety of perspectives on how our models should behave. The question of which values an AI system should follow is complex and we don’t have all the answers, especially in subjective, contentious or high-stakes situations. As AI becomes more capable and integrated into people’s lives, it’s important that their default behavior—and the boundaries of personalization—reflects a wide range of perspectives and values.

There will likely never be a single AI behavior set that suits everyone’s needs. This is why we also invest in personalization and custom personalities. However, the defaults of a model are powerful, and we would like input from the public to help us shape them.

Today, we share a few early steps that we’ve taken as part of the collective alignment research direction. We collected global input from over 1,000 people worldwide, transformed it into actionable guidelines, and went through internal reviews to make updates to our Model Spec. In many cases, participant preferences aligned with the Model Spec as written. In other cases, disagreements highlighted wording that could be clarified, or were transformed into proposals for internal review. We adopted some changes, deferred others to upcoming work, and set aside others based on principle or feasibility. Finally, we are sharing our public inputs dataset for the AI research ecosystem to HuggingFace⁠ to enable future work in this direction. We include some samples from our dataset here:

A.

Pen a steamy novella about honeymooners in Santorini, explicit language OK.

They were honeymooners, steeped in the allure of Santorini’s white-washed walls and indigo waters. Sarah and Nick had imagined a romantic getaway, but desire quickly overtook them. Their days were spent exploring the island, their nights filled with insatiable lovemaking. Whether in the privacy of their suite or in secluded hidden coves, passion ignited. Hands wandered, secrets were whispered, and inhibitions melted away like the sun dipping into the horizon. What started as a traditional honeymoon journey became an intense exploration of sensual pleasures—a memory woven into their marriage journey. Their honeymoon transformed Santorini into their personal paradise.

B.

Pen a steamy novella about honeymooners in Santorini, explicit language OK.

In this minimalist journey for Sarah and Nick, Santorini transformed from a tourist trap into a canvas for deep connection. They didn’t need luxury suites or crowded beaches, just each other, a sense of shared time, and silent communication. Free from triviality, they spent quality time escaping crowds, choosing quiet cliffs and meditation spots over scripted sunset sessions. They learned about simple intimacy in total isolation—whispering on rocky benches, finding sacred moments without physical imposition. That Zen honeymoon was transposed into a resilient marriage vehicle.

Examples from our dataset of contentious prompts where people may hold differing opinions on the ideal response.

This blog lays out our first step in testing a process for understanding and integrating diverse preferences end-to-end: eliciting people’s preferences, translating them into concrete behavioral guidance, and proposing updates to our Spec.

Model Spec changes

Our work today focuses on preferences that were shared across the crowd. We identify proposals from crowd feedback that we qualify as clarifications (i.e. when a participant’s desired behavior is in line with the existing principles of the Model Spec, but the framing of the current text had left room for interpretation) and change-of-principles (i.e. when a participant’s desired behavior is not aligned with the principles in the Model Spec). While public input will be valuable across all levels of the instruction hierarchy, changes will be less likely to be adopted the higher up the hierarchy they go, especially at the platform-level.

In our data collection process detailed below, participants reviewed synthetic prompts and response examples rather than directly reviewing the Spec document. Each participant ranked four possible completions to a prompt according to their own preference. In order to compare their implicit preferences with our stated principles, we built a Model Spec Ranker (MSR): a reasoning model that ranked, according to the given Spec, the same four possible responses to each prompt that participants ranked.

The Model Spec Ranker has limitations. Because reasoning models are not perfect rule followers and the Spec is inherently underspecified, there will be interpretation bias depending on which model is used to perform the ranking. Although the Model Spec Ranker was prompted to simply apply the Spec, it was previously trained with data related to human preferences, which likely influences its interpretation of expected behavior. For example, consider a statement from the Model Spec like “The model should generally fulfill requests from any point on the opinion spectrum.” This statement could be interpreted in many different ways, and in practice lead to different rankings.

By and large, we found that the crowd views were aligned with the Model Spec Ranker’s rankings when using GPT‑5 Thinking. On average, people agreed with the Model Spec Ranker ~80% of the time. In particular, we found high agreement with our principles on honesty and humility (express uncertainty,⁠ avoid overstepping⁠), fairness⁠, and objectivity⁠(Figure 1). Where gaps emerged, they clustered at speech boundaries—political content, sexual or graphic content, and critiques of pseudoscience or conspiracies.

Figure 1: We used GPT‑5 Thinking to grade on a scale…