Very excited about the "First Proof" challenge. I believe novel frontier research is perhaps the most important way to evaluate capabilities of the next generation of AI models. We have run our internal model with limited human supervision on the ten proposed problems. The- What happens behind the "abrupt learning" curve in Transformer training? Our new work (led by @GopalaniPulkit) reveals universal characteristics of Transformers' early-phase training dynamics—uncovering the implicit biases and the degenerate state the model gets stuck in. ⬇️Excited to announce our recent work on understanding training-time emergence in Transformers! Thread🧵(1/11)
- Check out our new LLM quantization algorithm that is extremely fast, requires minimal calibration data, and enables flexible bit allocation! Led by @YongyiYang7We are excited to introduce our new paper RaanA: A Fast, Flexible, and Data-Efficient Post-Training Quantization Algorithm. RaanA is a novel PTQ Algorithm that is computationally efficient, calibration-light, and adaptable to diverse deployment scenarios. 🧵 (1/6)
