Apple’s new study on LLMs reveals that even the most advanced AI models benefit from the oldest productivity hack in the book: making a checklist. Instead of relying solely on reward models from human feedback, Apple researchers tested whether a list of clear, yes-or-no criteria could better align language models with user instructions, and the results speak for themselves.

Apple’s checklist approach outperforms traditional reward models

The study, titled Checklists Are Better Than Reward Models For Aligning Language Models, introduces a new method: Reinforcement Learning from Checklist Feedback (RLCF). Instead of giving a thumbs up or down to an AI’s answer, the system scores it against a checklist, item by item.

This method was tested on the open-source model Qwen2.5-7B-Instruct across five major benchmarks. Compared to reward-based methods, Apple’s checklist-driven reinforcement learning delivered consistent gains, including:

• +4 points in hard satisfaction rate on FollowBench
• +6 points on InFoBench
• +3 points in win rate on Arena-Hard

These aren’t minor tweaks; they represent measurable improvements in how LLMs follow complex, multi-step instructions.

How the checklists are created, yes, more AI

To build the training data, Apple’s team created WildChecklists, a dataset of over 130,000 prompts with custom-made checklists attached. The twist? The checklists themselves were generated by a larger language model Qwen2.5-72B-Instruct.

Each checklist uses yes/no criteria like “Is the translation in Spanish?” or “Does the output include three bullet points?” The system scores and weights these items, then uses them as training signals to refine how smaller models behave.

So essentially, a bigger model teaches a smaller one how to do better, with help from a list.

What this means for LLM-based assistants

LLMs are increasingly being used for complex tasks and step-by-step workflows. Apple’s research shows that giving these models a structured list of expectations results in clearer, more reliable outputs, especially in multi-step situations. That’s critical as AI assistants evolve into agentic tools, handling tasks like trip planning, scheduling, and content generation.

LLM-based assistants: Limitations and what’s next

The team is upfront about trade-offs. This method only works well for complex instruction-following, not for safety alignment. It also depends on larger models to score responses, which may not be efficient at scale. Still, the idea is surprisingly elegant: teach machines to think like humans… by having them check their work.

Even in AI, sometimes the smartest move is sticking to the basics.