World-Model RL Cuts AutoResearch Training Time 3‑4×, Boosts Small LLMs
The authors replace costly environment runs in AutoResearch pipelines with a world‑model‑driven RL loop, adding online debiasing and inverse‑variance denoising to counter model errors. This yields 3–4× faster training and lets 4B–9B agents outrun 48B–120B open‑weight baselines on held‑out benchmarks, opening a path to scalable, low‑cost empirical AI research.