Checklists Are Better Than Reward Models For Aligning Language Models

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 on top of a strong instruction following model (Qwen2.5-7B-Instruct)