Uploaded August 2026 | Updated September 2026, 2 weeks ago
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: ycombinator.com/apply
Work at a startup: ycombinator.com/jobs
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: ycombinator.com/apply
Work at a startup: ycombinator.com/jobs
