Study: Pretraining on K-5 Data Limits Generalization Despite RL
mathemagic1an · x · 2026-08-20
The paper 'LittleLearner' trained a 5B model exclusively on K-5 content, creating a strict knowledge boundary. Experiments showed that scaling, few-shot prompting, and post-training techniques like SFT+GRPO failed to bridge the gap for out-of-scope reasoning. The findings suggest that underlying 'thought algorithms' or problem-solving techniques must be present in the pretraining data, and post-training primarily amplifies existing capabilities rather than creating new ones.
More from Research
- FDA cleared 1,357 medical AI devices, only 3 tested on patient outcomes — EricTopol · 2026-08-20
- Codex asks researcher to blind treatment status from itself during data analysis — Afinetheorem · 2026-08-20
- UC Berkeley releases open-source humanoid robot for under $5,000 — lukas_m_ziegler · 2026-08-20
- UW–Madison Hosts Brain Decoding Challenge for #MLM26 — Pseudomanifold · 2026-08-20
- Study: Social media feeds often clash with user values — mattgroh · 2026-08-20
- FrankenRedis: A memory-safe Rust reimplementation of Redis, built with AI agents — doodlestein · 2026-08-20