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 # Golf-Forecaster
 
-**LoRA adapter** for [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b), RL-tuned to predict professional golf outcomes — tournament winners, cuts, matchups, majors, team events, season races, world rankings, and player milestones across every major tour. Trained on 3,178 binary forecasting questions from [GolfForecasting](https://huggingface.co/datasets/LightningRodLabs/GolfForecasting) using the [Lightning Rod SDK](https://github.com/lightning-rod-labs/lightningrod-python-sdk). Beats GPT-5.
+### RL-Tuned gpt-oss-120b for Predicting Professional Golf Outcomes
 
-[Dataset](https://huggingface.co/datasets/LightningRodLabs/GolfForecasting) · [Lightning Rod SDK](https://github.com/lightning-rod-labs/lightningrod-python-sdk) · [Future-as-Label paper](https://arxiv.org/abs/2601.06336) · [Outcome-based RL paper](https://arxiv.org/abs/2505.17989)
+Starting from nothing but 9 search queries, we used the [Lightning Rod SDK](https://github.com/lightning-rod-labs/lightningrod-python-sdk) to automatically generate [3,178 forecasting questions](https://huggingface.co/datasets/LightningRodLabs/GolfForecasting) from news articles, label them using real outcomes, and train this model via RL. **No expertise required. No manual labeling. No domain-specific engineering.** The result beats GPT-5 on held-out questions.
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+You can do this in any domain — just change the search queries. See [how we built the dataset](https://huggingface.co/datasets/LightningRodLabs/GolfForecasting).
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+This repo contains a **LoRA adapter** for [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b). A standalone `merge.py` script is included to merge it into a full model.
 
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