| # YarrowLab |
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|
| Post-training research on **bounded deviation from a statistical prior** for |
| event forecasting. |
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| Instead of asking a model to output a probability directly, we train it to |
| output `Δ` — a bounded deviation from a prior `p₀` computed upstream from |
| reference-class statistics — plus an attribution category for *why* the |
| prior might be wrong, and an evidence span supporting that attribution. |
| The final probability `p = σ(logit(p₀) + Δ)` is composed in code; the model |
| never sees or touches market prices. |
|
|
| ## Repositories |
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| - [`yarrow-delta-sft-v1-smoke`](https://huggingface.co/YarrowLab/yarrow-delta-sft-v1-smoke) — |
| a pipeline-validation checkpoint (LoRA over a randomly initialized tiny |
| model). Confirms the SFT loop runs end to end; not a trained model. |
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| More will be added here as real training runs land. |
