LLM training runs can look healthy for billions of tokens or more then suddenly diverge. We find that quantization error in the LM head drives these delayed time-bomb loss spikes. A simple optimizer change can prevent them. 🧵 1/n
Excited to announce Next-Concept Modeling (NCM): our new paradigm for fast and parallel, coarse-to-fine text generation! 🚀
Compared to strong diffusion language models (DLMs) at similar parameter sizes, NCM showcases:
⚡ 31× faster generation on downstream summarization and translation tasks.
🏎️ 10–21× faster unconditional generation across TinyStories, LM1B, and OpenWebText, with comparable or better generative perplexity using 3–5× fewer training tokens.
🧩 Better reasoning performance on tasks like Sudoku.
Check out the paper and codebase 👇
Arxiv: https://t.co/NUCOYycgcM
AlphaXiv: https://t.co/4brvP8GxDg
Code: https://t.co/CnkJHS4KQt
Enjoyed working with @KumbongHermann and advised by @simran_s_arora@Azaliamirh and @HazyResearch.
🚨 LLMs are trained on past data and deployed in the future, but we evaluate them like time doesn't exist. By treating LLM evaluation as a forecasting problem, we can better predict how models will actually perform in the future. 📈 1/n
Announcing Exploration-Distillation, a RL framework that unlocks aggressive exploration and large performance boosts over standard algorithms like DAPO. In principle, an LLM can discover new things during RL, but existing algorithms struggle. Here’s how we do it. 🧵1/n