# LLM Detoxification via Transition-Aware Reward-Guided Representation Sampling

**Authors:** Aishan Liu, Yisong Xiao, Xianglong Liu, Yongxin Huang, Siyuan Liang, Zonghao Ying, Yang Qu, Jian Yang, Yaodong Yang, Dacheng Tao  
**Lab:** Beihang University · **Published:** 30 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.41165) · [PDF](https://arxiv.org/pdf/2610.41165) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.41165.html)

## Key numbers

- **69.38%**: reducing toxicity by up to 69.38% on RealToxicityPrompts
- **64.23%**: reducing toxicity by up to 64.23% across 3 representative LVLMs

## Abstract

Large Language Models (LLMs) can generate toxic content, motivating effective test-time detoxification. Existing representation steering methods often rely on static or coarse interventions and provide imprecise guidance because they inadequately model transitions between toxic and non-toxic generations. We propose Transition-Aware Reward-Guided Representation Sampling (TARGRS), a test-time framework that learns reward guidance over toxicity transitions in latent representation space. During exploration, progressive steering along the toxicity direction constructs trajectories from non-toxic to toxic states. Toxicity scores along these trajectories turn sparse annotations into dense, listwise supervision for a transition-aware token-level reward model. During inference, TARGRS uses this model to guide coarse-to-fine sampling along the opposite, non-toxic direction: coarse sampling identifies the intervention needed to reach non-toxic regions, and refinement selects a detoxified representation with minimal perturbation. Experiments on 10 LLMs across RealToxicityPrompts, BOLD, and AttaQ show that TARGRS consistently outperforms state-of-the-art baselines, reducing toxicity by up to 69.38% on RealToxicityPrompts. TARGRS runs nearly as fast as the original LLM while preserving its core capabilities with minimal impact. By intervening in the language decoder space, TARGRS also extends to Large Vision-Language Models (LVLMs), reducing toxicity by up to 64.23% across 3 representative LVLMs.

---

Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.