Jacob Springer @jacobspringer.bsky.social · 26/03/2025Fine-tuning behaves similarly: using a fixed learning rate across different pre-training checkpoints, we see eventual degradation in both task performance and web-data perplexity. This often holds even after hyperparameter tuning. Overtraining = worse fine-tuning outcomes! 8/10 110
Jacob Springer @jacobspringer.bsky.social · 26/03/2025👉 Early in training: Models have low sensitivity & the base model improves quickly; performance improves 📈 👉 Late in training: Models become highly sensitive & the base model improves slowly; performance degrades! 📉 7/10 110
Jacob Springer @jacobspringer.bsky.social · 26/03/2025🔹 Early checkpoints: Robust to parameter changes. 🔸 Later checkpoints: Highly sensitive, leading to worse performance after perturbation! (Left plot: sensitivity increases over training, Right plot: final performance eventually degrades.) 5/10 111
Jacob Springer @jacobspringer.bsky.social · 26/03/2025Example: OLMo-1B trained on 3T tokens performs over 2% *worse* after instruction tuning than its 2.3T-token version—even though it saw 30% more data! We see similar observations for many other post-training setups. Why does extended pre-training hurt fine-tuning performance? 🤔 3/10 100
Jacob Springer @jacobspringer.bsky.social · 26/03/2025Training with more data = better LLMs, right? 🚨 False! Scaling language models by adding more pre-training data can decrease your performance after post-training! Introducing "catastrophic overtraining." 🥁🧵👇 arxiv.org/abs/2503.19206 1/10 13314