Next Seminar

Authors: Daniel Russo

Abstract: A widely used technique for improving policies is success conditioning, in which one collects trajectories, identifies those that achieve a desired outcome, and updates the policy to imitate the actions taken along successful trajectories. This principle appears under many names -- rejection sampling with SFT, goal-conditioned RL, Decision Transformers -- yet what optimization problem it solves, if any, has remained unclear. We prove that success conditioning exactly solves a trust-region optimization problem, maximizing policy improvement subject to a χ2 divergence constraint whose radius is determined automatically by the data. This yields an identity: relative policy improvement, the magnitude of policy change, and a quantity we call action-influence -- measuring how random variation in action choices affects success rates -- are exactly equal at every state. Success conditioning thus emerges as a conservative improvement operator. Exact success conditioning cannot degrade performance or induce dangerous distribution shift, but when it fails, it does so observably, by hardly changing the policy at all. We apply our theory to the common practice of return thresholding, showing this can amplify improvement, but at the cost of potential misalignment with the true objective.

Speaker Bio: Daniel Russo is an associate professor in the Decision, Risk, and Operations division of Columbia Business School. He completed his undergraduate studies in math and economics at the University of Michigan, doctoral studies at Stanford University under the supervision of Benjamin Van Roy, and worked as a postdoctoral researcher at Microsoft Research New England. His research has been recognized by several awards in the operations research community: the George Nicholson Prize (best paper by a PhD student), the JFIG Paper Award (best paper by a junior faculty member), the Frederick W. Lanchester Prize (best contribution to operations research in the past five years), and the Erlang Prize (early career award for contributions to applied probability). He currently serves as an associate editor at Management Science, Operations Research, and Stochastic Systems.

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