Bigger wasn’t better this time. Core contributor @valoryag fine-tuned NVIDIA’s Nemotron 120B on forecasting data from Olas agents, using the recipe behind Olas-Predict-R1-14B: selected reasoning examples, a supervised warm-up, then reinforcement learning. Training ran through @thinkymachines’ Tinker. On 11,496 held-out predictions, Nemotron showed no clear improvement. Before → after training: • Brier error (lower is better): 0.265 → 0.267 • Balanced accuracy (higher is better): 59.7% → 59.4% • Unreadable answers (fewer is better): 503 → 528 Our trained 14B model had better calibration, higher balanced accuracy and just 10 unreadable answers. Nemotron retained a slightly lower Brier error: 0.267 versus 0.270. Why didn’t the recipe transfer? Nemotron’s mixture-of-experts design may need more varied data or different training settings for forecasting. We haven’t tested those explanations, so the cause remains open. What we learned: our existing recipe and training budget improved the 14B model but brought no clear gain to Nemotron. Next, we’ll test a larger dense model, with an early check for improvement before committing to the full run. Read the experiment and results: https://t.co/ZSziFiraIZ