4 papers
Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation
Yecheng Wu, Song Han, Hai Cai
On-policy distillation (OPD) is an effective post-training paradigm for large language models but requires a live teacher server throughout training, resulting in substantial infra…
Empowering Multi-Turn Tool-Integrated Agentic Reasoning with Group Turn Policy Optimization
Yifeng Ding, Hung Le, Songyang Han +5
Training Large Language Models (LLMs) for multi-turn Tool-Integrated Reasoning (TIR) - where models iteratively reason, generate code, and verify through execution - remains challe…
Quantization-Aware Distillation for NVFP4 Inference Accuracy Recovery
Meng Xin, Sweta Priyadarshi, Jingyu Xin +26
This technical report presents quantization-aware distillation (QAD) and our best practices for recovering accuracy of NVFP4-quantized large language models (LLMs) and vision-langu…
Stable Asynchrony: Variance-Controlled Off-Policy RL for LLMs
Luke J. Huang, Zhuoyang Zhang, Qinghao Hu +2
Asynchronous reinforcement learning has become increasingly central to scaling LLM post-training, delivering major throughput gains by decoupling rollout generation from policy upd…