Ruohao Guo

🚀 Introducing Nemotron-Cascade! 🚀 We’re thrilled to release Nemotron-Cascade, a family of general-purpose reasoning models trained with cascaded, domain-wise reinforcement learning (Cascade RL), delivering best-in-class performance across a wide range of benchmarks. 💻 Coding powerhouse After RL, our 14B model: • Surpasses DeepSeek-R1-0528 (671B) on LiveCodeBench v5/v6/Pro. • Achieves silver-medal performance at IOI 2025 🥈. • Reaches a 43.1% pass@1 on SWE-Bench Verified, and 53.8% with test-time scaling. 🧠 What is Cascade RL? Instead of mixing heterogeneous prompts across domains, Cascade RL trains sequentially, domain by domain, which reduces engineering complexity, mitigates heterogeneous verification latencies, and enables domain-specific curricula and tailored hyperparameter tuning. ✨ Key insight Using RLHF for alignment as a pre-step dramatically boosts complex reasoning—far beyond preference optimization. Subsequent domain-wise RLVR stages rarely hurt the benchmark performance attained in earlier domains and may even improve it, as illustrated in the following figure. 🤗 Models & training data 🔥 👉 huggingface.co/collections/nv… 📄 Technical report with detailed training and data recipes 👉 arxiv.org/pdf/2512.13607

🌀Agent Learning via Early Experience🌀 📝: arxiv.org/abs/2510.08558 - SFT for agents is sparse; RL on long-horizons is hard We provide new mid-training signals that work: 1) Implicit next state world modeling task 2) Self-reflection on alternate states - Strong improvements over 8 environments and multiple model families - Works well for subsequent RL! 🧵1/5

Congratulations to @ychenNLP for successfully defending his PhD! Yang has done exciting work advancing both the multilingual and multimodal capabilities of LLMs. Many thanks to his committee: @cocoweixu (co-advisor), @mchang21, @Hexiang_Hu, @kartik_goyal_