Diamond Maps: Efficient Reward Alignment for Generative Models

At our inaugural YCML at Startup School, YC Partner Ankit Gupta speaks with Douglas Chen about Diamond Maps, a method for steering generative models toward desired outputs more efficiently. Reward alignment depends on estimating how promising an intermediate state in the generation process is. Existing flow-map methods make this estimate using a single possible final output. Diamond Maps instead samples multiple outcomes from the same intermediate state, producing a better estimate and stronger guidance. The work includes both a fine-tuning method and a training-free inference-time method, allowing existing generative models to be aligned without necessarily retraining them. Apply to Y Combinator: Work at a startup: