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Multi-Objective Reinforcement Learning Using Routed Ensembles and Trajectory Attribution

Paper recorded by Signals 4 on 2026-10-26 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-26 on arXiv · recorded by Signals 4 on 2026-10-27

Category: cs.AI · 人工智能 · first seen 2026-10-27

Abstract

Multi-objective reinforcement learning is a fundamental problem for modern AI, with applications spanning post-training, reasoning models, and robotic control. A central challenge is to learn policies that balance competing objectives, such as helpfulness versus honesty in safety and alignment, or speed versus energy in robotics. The optimal policy can vary sharply with objective-weighting combina

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#157 most recent of 960 cs.AI papers we have recorded · ↑ newer: CLOSURE: Cross-Channel Closure for Multivariate Time Series Anomaly De · ↓ older: The Watchtower Imperative: Lifespan-Calibrated Evaluation for Frontier
Cite this page: Multi-Objective Reinforcement Learning Using Routed Ensembles and Trajectory Attribution: the #157 most recent of 960 cs.AI papers we have recorded (as of 2026-10-26). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/multi-objective-reinforcement-learning-using-routed-ensembles-and-trajectory-att.html
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