5 papers
Attention Amnesia in Hybrid LLMs: When CoT Fine-Tuning Breaks Long-Range Recall, and How to Fix It
Xinyu Zhou, Boyu Zhu, Yi Xu +4
Chain-of-thought (CoT) supervised fine-tuning (SFT) is widely adopted to improve reasoning ability, yet we find that it systematically degrades long-context recall in hybrid linear…
Triaging Threats to Specialized Guardrails
Wenjie Jacky Mo, Xiaofei Wen, Rui Cai +6
Building robust safety guardrails is essential for deploying Large Language Models across diverse real-world applications. However, this goal remains challenging because safety ris…
EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL
Minrui Xu, Zilin Wang, Mengyi DENG +12
Equipping LLMs with tool-use capabilities via Agentic Reinforcement Learning (Agentic RL) is bottlenecked by two challenges: the lack of scalable, robust execution environments and…
CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models
Xiao Zhu, Xinyu Zhou, Boyu Zhu +5
Reinforcement Learning from Verifiable Rewards (RLVR) has driven recent progress in code large language models by leveraging execution-based feedback from unit tests, but its scala…
CodeSpecBench: Benchmarking LLMs for Executable Behavioral Specification Generation
Zaoyu Chen, Jianbo Dai, Boyu Zhu +6
Large language models (LLMs) can generate code from natural language, but the extent to which they capture intended program behavior remains unclear. Executable behavioral specific…