willcb: every non-RL algorithm is a proxy — policy gradients are the minimum-variance unbiased estimator
willcb · x · 2026-10-12
willcb pushes back on treating RL as just "an algorithm" that might be replaced. Every algorithm implicitly aims to optimize an objective, and RL is simply what falls out of taking the gradient of that objective. Any other approach means giving up direct optimization in favor of a proxy — useful when your objective is imprecise or you have teacher signals (static datasets, teacher models, process evaluation) that trade unbiasedness for robustness or lower sampling cost. But if your objective can be made explicit, there are no shortcuts: policy gradients are minimum-variance unbiased estimators of progress.
Related event: willcb: RL Is Direct Objective Optimization, On-policy GRPO Comes Closest(2 posts)→
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