Trained an LLM to predict "What will Trump do?"
Hey HN! I RL-tuned an open-source LLM (gpt-oss-120b — 120B MoE, but only 5.1B active params) to predict "What will Trump do?" in any situation, trained on nothing but public news collected automatically from search queries. The trained model beats GPT-5, and both dataset and trained model are open sourced. Data generation: Generated 2,108 binary forecasting questions from just a search query and a date range using the Lightning Rod SDK (https://github.com/lightning-rod-labs/lightningrod-python-sd...). Questions are generated from historic news articles — like "Will Trump…
In plain words
This project fine-tunes an open-source 120-billion-parameter language model to forecast political outcomes, specifically predicting Trump's actions in various scenarios. The model was trained on 2,108 automatically generated forecasting questions derived from historical news articles, with answers determined by what actually occurred. Both the dataset and trained model are publicly available on Hugging Face. The system uses reinforcement learning optimization rather than manual annotation, making the entire pipeline automated from data generation through model training.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN! I RL-tuned an open-source LLM (gpt-oss-120b — 120B MoE, but only 5.1B active params) to predict "What will Trump do?" in any situation, trained on nothing but public news collected automatically from search queries. The trained model beats GPT-5, and both dataset and trained model are open sourced. Data generation: Generated 2,108 binary forecasting questions from just a search query and a date range using the Lightning Rod SDK (https://github.com/lightning-rod-labs/lightningrod-python-sd...). Questions are generated from historic news articles — like "Will Trump impose 25% tariffs on Mexico by March 1?" — and resolved by checking what actually happened after the deadline. No human annotation — the whole pipeline is automated. Training: GRPO with Brier score as the reward signal. LoRA rank 32, 50 training steps. Results: Slight accuracy edge over GPT-5 (Brier 0.194 vs 0.200), but big gains in calibration — the RL-tuned model produces much better probabilities (ECE 0.079 vs 0.091). Dataset: https://huggingface.co/datasets/LightningRodLabs/WWTD-2025 This is a fully automated way to spin up domain expert LLMs from public web data with just a few search queries, no labeling/annotation required. I’d love any feedback, or suggestions for what domain expert to train next!
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