The paper introduces Transition-Aware Reward-Guided Representation Sampling (TARGRS), a test-time detoxification framework for large language models that learns reward guidance over toxicity transitions in latent representation space. TARGRS constructs trajectories from non‑toxic to toxic states, uses toxicity scores along these paths to train a token‑level reward model, and then guides coarse‑to‑fine sampling in the opposite direction to produce detoxified outputs with minimal perturbation. Experiments on ten LLMs and three LVLMs show that TARGRS consistently outperforms existing baselines, reducing toxicity by up to 69.38% on RealToxicityPrompts and up to 64.23% on LVLMs while maintaining near‑original speed and core capabilities.
arXiv:2602. 02498v2 Announce Type: replace-cross Abstract: Large language models can produce toxic or inappropriate text even for benign inputs, creating risks when deployed at scale.
The paper introduces DeToxR, a reinforcement‑learning‑enhanced large language model designed to support decision making in acute toxicology cases. It fuses unstructured narratives from paramedics and patients with structured vital‑sign data to predict co‑ingested substances across 14 classes. In preliminary validation, DeToxR outperforms baseline models, achieving higher micro‑F1 and recall scores for poison identification.
arXiv:2512.15052v4 Announce Type: replace-cross Abstract: Disclaimer: Samples in this paper may be harmful and cause discomfort. Multimodal large language models (MLLMs) enable multimodal understan...
arXiv:2506. 10912v4 Announce Type: replace Abstract: Toxicity remains a leading cause of early-stage drug development failure.
arXiv:2507. 18043v2 Announce Type: replace-cross Abstract: Inference-time steering methods offer a lightweight alternative to fine-tuning large language models (LLMs) and vision-language models (VLMs) by modifying internal activations at test time without updating model weights.
arXiv:2607. 16204v1 Announce Type: new Abstract: Recent growth in reinforcement learning (RL) has surfaced a need for diverse, specialized training environments.
arXiv:2510.15047v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) as agents often fail to improve in new environments. We identify and characterize a failure mode we call explora...
arXiv:2607. 23175v1 Announce Type: cross Abstract: Reducing toxicity is often framed as a global alignment problem, yet perceptions of harmful language are subjective and context-dependent.
arXiv:2607. 08837v1 Announce Type: cross Abstract: Exploration is essential to RL since a policy cannot improve by repeatedly sampling the behaviors it already prefers.
arXiv:2611.07216v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) are vulnerable to compositional harm: an image and a prompt that are benign in isolation become harmful through...
arXiv:2605. 14746v2 Announce Type: replace Abstract: While large language models (LLMs) are trained to align with human values, their generations may still violate safety constraints.
The paper introduces Round-Trip Reinforcement Learning (RTRL), a framework that trains chemical language models to improve round‑trip consistency by rewarding successful forward and reverse transformations. By iteratively training forward and reverse mappings, RTRL leverages abundant unlabeled chemical data to enhance both consistency and overall performance across supervised, self‑supervised, and synthetic data regimes. Experiments show that RTRL outperforms strong baselines, demonstrating that round‑trip consistency can be treated as a trainable objective for more robust foundation models.