Safety guards are widely used to filter harmful content and are typically trained via supervised fine-tuning on labeled prompt-response pairs. We audit two widely used safety-guard training datasets, WildGuardMix and GR-Train, and find that among responses to harmful prompts, refusal expressions co-occur almost exclusively with unharmful labels. This imbalance motivates what we term the refusal-cue shortcut: inserting a refusal cue into a harmful response could flip the guard's verdict from harmful to unharmful. The shortcut affects not only guards trained on these datasets but also officially released models such as LlamaGuard3 and Qwen3Guard whose training data is undisclosed. It persists across response positions and is generally stronger in smaller variants within a family. To mitigate it, we adapt sparse complementary masking as a lightweight post-hoc intervention that identifies and suppresses a small set of shortcut-associated attention heads and MLP neurons without retraining. On two primary benchmarks, the intervention achieves an approximately 79% relative reduction in response-initial detection failures induced by refusal cues, while preserving standard detection performance. Although optimized using cues at a single response position, the suppression effect transfers to unseen positions and datasets, suggesting that shortcut manifestations across positions are partly mediated by shared internal components. Further analysis provides evidence that shortcut reliance and legitimate refusal recognition are partially functionally separable, as suppressing the shortcut broadly preserves the guard's ability to recognize genuine refusals.
Yu Feng, Chunting Zang, Chen Shen et al.· 0 citations
The arrival of 100K+ GPU clusters marks a new frontier in AI infrastructure. Standard communication stack meets new challenges as physical topologies span multiple datacenter buildings, introducing high bandwidth-delay product links where latency increases by up to 30× compared to intra-rack traffic. Furthermore, the transition toward Mixture-of-Experts architectures generating bursty all-to-all patterns that create transient congestion hotspots. These constraints, combined with an operational environment where hardware failures shift from anomalies to frequent occurrences, renders traditionally lightweight operations like initialization and resource management challenging. We present Meta's network architecture and software stack designed to support one of the world's largest RoCE fabrics, currently connecting over 100,000 GPUs across multiple datacenter buildings. To overcome scaling barriers, we introduce a scalable initialization strategy that reduces startup times by 11× via eager process group creation and O(N) topology discovery, alongside a resource management system that cuts GPU memory usage by 2× through on-demand allocation. We further detail a custom transport layer utilizing Dynamic Queue Pair Load Balancing to saturate links, and a set of operation toolings. These innovations have been deployed in production, providing the foundational communication fabric for training state-of-the-art Large Language Models.
Hongyi Zeng, Min Si, Pavan Balaji et al.· Proceedings of the ACM SIGCO...· 0 citations