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TACS: Trajectory-Aware Candidate Selection for LLM Jailbreak Suffix Optimization

Paper recorded by Signals 4 on 2026-08-30 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-30 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.LG · 机器学习 · first seen 2026-09-01

Abstract

Gradient-based jailbreak suffix optimization methods typically update the suffix by retaining the candidate with the lowest current loss. We show that this seemingly natural design is fundamentally myopic: candidates that look better under the current-step proxy often fail to produce better jailbreak outcomes later in the search, revealing a form of selection-stage reward hacking. This suggests th

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#194 most recent of 215 cs.LG papers we have recorded · ↑ newer: Event-triggered Control and Online Learning for Networked Systems unde · ↓ older: Asynchronous Cooperative Online Learning for Multi-Robot Control under
Cite this page: TACS: Trajectory-Aware Candidate Selection for LLM Jailbreak Suffix Optimization: the #194 most recent of 215 cs.LG papers we have recorded (as of 2026-08-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/tacs-trajectory-aware-candidate-selection-for-llm-jailbreak-suffix-optimization.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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