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On the Convergence of Success Conditioning for Policy Optimization

Paper recorded by Signals 4 on 2026-10-02 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-02 on arXiv · recorded by Signals 4 on 2026-10-05

Category: cs.LG · 机器学习 · first seen 2026-10-05

Abstract

Success conditioning is a strategy for improving decision-making policies in stochastic environments; it updates a policy by increasing the probability of taking actions that yield successful outcomes. Success conditioning is common to many reinforcement learning applications, yet its limiting behavior and convergence rates are not well understood. In this work, we demonstrate that success conditi

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#393 most recent of 762 cs.LG papers we have recorded · ↑ newer: When May a Bandit Leave Its Anchor? E-Process-Authorized Thompson Samp · ↓ older: IDRF: Inverse-Distilled Reward Fine-tuning of Masked Discrete Diffusio
Cite this page: On the Convergence of Success Conditioning for Policy Optimization: the #393 most recent of 762 cs.LG papers we have recorded (as of 2026-10-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/on-the-convergence-of-success-conditioning-for-policy-optimization.html
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