Checklists Are Better Than Reward Models For Aligning Language Models
Vijay Viswanathan, Yanchao Sun, Xiang Kong, Meng Cao, Graham Neubig, Tongshuang Wu
Language models must be adapted to understand and follow user instructions. Reinforcement learning is widely used to facilitate this— typically using fixed criteria such as "helpfulness" and "harmfulness". In our work, we instead propose using flexible, instruction-specific criteria as a means of broadening the impact that reinforcement learning can have in eliciting instruction following. We propose "Reinforcement Learning from Checklist Feedback" (RLCF). From instructions, we extract checklists and evaluate how well responses satisfy each item—using both AI judges and specialized verifier programs—then combine these scores to compute rewards for RL. We compare RLCF with other alignment methods applied to a state-of-the-art instruction following model (Qwen2.5-7B-Instruct) — RLCF is the only method to improve on every benchmark, including a 4 point increase in hard satisfaction rate on FollowBench and a 3 point boost in win rate on Arena-Hard. These results establish checklist feedback as a key tool for improving language models' support of queries that express a multitude of needs. We will release our models and our dataset of checklists, "WildChecklists", to the public.